Intelligent joint prosthesis

ABSTRACT

Medical devices coupled to a sensor, and systems including such devices, can generate data and analysis based on that data, which may be used to identify and/or address problems associated with the implanted medical device, including incorrect placement of the device, unanticipated degradation of the device, and undesired movement of the device. Also provided are medical devices coupled to a sensor, and devices and methods to address problems that have been identified with an implanted medical device.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. application Ser. No.17/424,058, filed Jul. 19, 2021, which is a national phase under 35U.S.C. § 371 of International Application No. PCT/US2020/036516, filedJun. 6, 2020, which claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Patent Application No. 62/858,277 filed Jun. 6, 2019, whichapplications are incorporated herein by reference in their entirety forall purposes.

FIELD OF THE INVENTION

The present invention relates generally to medical devices with asensor, systems including such devices, methods of using such devicesand systems and the data generated therefrom, and devices and methods toaddress problems associated with an implanted medical device with asensor.

BACKGROUND

Medical devices and implants have become common-place in modernmedicine. Typically, medical devices and implants are manufactured toreplace, support, or enhance an anatomical or biological structure. Whenthe medical device is located on the surface of the patient, the deviceis readily viewable by the patient and the attending health careprofessional. However, when the medical device is designed to beimplanted in a patient, i.e., is an implantable medical device or amedical implant, it is typically not readily viewable.

Examples of medical implants include orthopedic implants such as hip,shoulder and knee prosthesis; spinal implants (spinal cages andartificial discs) and spinal hardware (screws, plates, pins, rods);intrauterine devices; orthopedic hardware used to repair fractures andsoft tissue injuries (casts, braces, tensor bandages, plates, screws,wires, dynamic hip screws, pins and plates); cochlear implants;aesthetic implants (breast implants, fillers); and dental implants.

Using the knee as a specific example, current prosthetic systems for atotal knee arthroplasty (TKA) typically consist of up to fivecomponents: a femoral component, a tibial component, a tibial insert, atibial stem extension and a patella component, where collectively thesefive components may be referred to as a total knee implant (TKI). Thesecomponents are designed to work together as a functional unit, toreplace and provide the function of a natural knee joint. The femoralcomponent is attached to the femoral head of the knee joint and formsthe superior articular surface. The tibial insert (also called a spacer)is often composed of a polymer and forms the inferior articulatingsurface with the metallic femoral head. The tibial component consists ofa tibial stem that inserts into the marrow cavity of the tibia and abase plate, which is sometimes called either a tibial plate, a tibialtray, or a tibial base plate that contacts/holds the tibial insert.Optionally, and particularly where the proximal tibial bone qualityand/or bone quantity is compromised, a tibial stem extension can beadded to the tibial stem of the tibial component, where the tibial stemextension serves as a keel to resist tilting of the tibial component andincrease stability. Commercial examples of TKA products include thePersona™ knee system (I113369) and associated tapered tibial stemextension (K133737), both by Zimmer Biomet Inc. (Warsaw, Ind., USA). Thesurgery whereby these four components are implanted into a patient isalso referred to as a total knee replacement (TKR). Similar prostheticdevices are available for other joints, such as total hip arthroplasty(THA) and shoulder arthroplasty (TSA), where one articular surface ismetallic, and the opposing surface is polymeric. Collectively, thesedevices and procedures (TKA, THA and TSA) are often referred to as totaljoint arthroplasty (TJA).

For a TKA, the tibial component and the femoral component are typicallyinserted into, and cemented in place within, the tibia bone and femoralbone, respectively. In some cases, the components are not cemented inplace, as in uncemented knees. Regardless of whether they are cementedin place or not, once placed and integrated into the surrounding bone (aprocess called osteointegration), they are not easy to remove.Accordingly, proper placement of these components during implantation isvery important to the successful outcome of the procedure, and surgeonstake great care in implanting and securing these components.

Current commercial TKA systems have a long history of clinical use withimplant duration regularly exceeding 10 years and with some reportssupporting an 87% survivorship at 25 years. Clinicians currently monitorthe progress of TKA patients post implant using a series of physicalexams at 2-3 weeks, 6-8 weeks, 3 months, 6 months, 12 months, and yearlythereafter.

After the TKI has been implanted, and the patient begins to walk withthe knee prosthesis, problems may occur and are sometimes hard toidentify. Clinical exams are often limited in their ability to detectfailure of the prosthesis; therefore, additional monitoring is oftenrequired such as CT scans, MRI scans or even nuclear scans. Given thecontinuum of care requirements over the lifetime of the implant,patients are encouraged to visit their clinician annually to reviewtheir health condition, monitor other joints, and assess the TKAimplant's function. While the current standard of care affords theclinician and the healthcare system the ability to assess a patient'sTKA function during the 90-day episode of care, the measurements areoften subjective and lack temporal resolution to delineate small changesin functionality that could be a pre-cursor to larger mobility issues.The long-term (>1 year) follow up of TKA patients also poses a problemin that patients do not consistently see their clinicians annually.Rather, they often seek additional consultation only when there is painor other symptoms.

Currently, there is no mechanism for reliably detecting misplacement,instability, or misalignment in the TKA without clinical visits and thehands and visual observations of an experienced health care provider.Even then, early identification of subclinical problems or conditions iseither difficult or impossible since they are often too subtle to bedetected on physical exam or demonstratable by radiographic studies.Furthermore, if detection were possible, corrective actions would behampered by the fact that the specific amount movement and/or degree ofimproper alignment cannot be accurately measured or quantified, makingtargeted, successful intervention unlikely. Existing external monitoringdevices do not provide the fidelity required to detect instability sincethese devices are separated from the TKA by skin, muscle, and fat—eachof which masks the mechanical signatures of instability and introduceanomalies such as flexure, tissue-borne acoustic noise, inconsistentsensor placement on the surface, and inconsistent location of theexternal sensor relative to the TKA.

Implants other than TKA implants may also be associated with variouscomplications, both during implantation and post-surgery. In general,correct placement of a medical implant can be challenging to the surgeonand various complications may arise during insertion of any medicalimplant (whether it is an open surgical procedure or a minimallyinvasive procedure). For example, a surgeon may wish to confirm correctanatomical alignment and placement of the implant within surroundingtissues and structures. This can however be difficult to do during theprocedure itself, making intraoperative corrective adjustmentsdifficult.

In addition, a patient may experience a number of complicationspost-procedure. Such complications include neurological symptoms, pain,malfunction (blockage, loosening, etc.) and/or wear of the implant,movement or breakage of the implant, inflammation and/or infection.While some of these problems can be addressed with pharmaceuticalproducts and/or further surgery, they are difficult to predict andprevent; often early identification of complications and side effects,although desirable, is difficult or impossible.

The present disclosure is directed to identifying, locating and/orquantifying these problems, particularly at an early stage, andproviding methods and devices to remedy these problems.

All of the subject matter discussed in the Background section is notnecessarily prior art and should not be assumed to be prior art merelyas a result of its discussion in the Background section. Along theselines, any recognition of problems in the prior art discussed in theBackground section or associated with such subject matter should not betreated as prior art unless expressly stated to be prior art. Instead,the discussion of any subject matter in the Background section should betreated as part of the inventor's approach to the particular problem,which in and of itself may also be inventive.

SUMMARY

Briefly stated, the present disclosure relates to intelligent implants,systems including intelligent implants, methods of using theimplants/systems to do at least one of detect, locate, quantify and/orcharacterize problems associated with the implant, and methods anddevices to address the problems that have been identified. As providedin more detail below, the present disclosure provides medical devicescoupled to a sensor, and systems including such devices, which cangenerate data as well as analysis based on that data, which may be usedto identify and/or address problems associated with the implantedmedical device. In one embodiment the medical device is an artificialjoint (TJA) and the data is kinematic data reflecting movement of theartificial joint. Problems that may be identified include incorrectplacement of the TJA device, incorrect alignment of the device,unanticipated degradation or wear of the device, instability of thedevice (and the associated joint), and undesired movement of the device.Also provided are medical devices coupled to a sensor, and devices andmethods to address problems that have been identified with an implantedmedical device.

The medical device coupled to a sensor may be referred to as anintelligent implant, where the intelligent implant will include a sensorthat can detect and/or measure the functioning of the implant and/or theimmediate environment around the implant and/or the activity/movement ofthe implant as well as the activity and movement of the patient. Theimplant may alternatively be referred to herein as a prosthesis, wherean intelligent implant and an intelligent prosthesis have the samemeaning. In one embodiment, the coupling of the sensor to the medicaldevice, e.g., to the prosthesis/implant, is to have the sensor locatedentirely within the medical device such that the sensor is totallyenclosed by the exterior surface of the medical device, so that no partof the sensor physically contacts any tissue of a patient into whom themedical device has been implanted. In embodiments of the presentdisclosure, reference herein to a medical device, or to an implant or aprosthesis may be understood to be a reference to an intelligent medicaldevice or implant/prosthesis having a sensor that is located entirelywithin the medical device or implant/prosthesis as disclosed herein. Inembodiments of the present disclosure, reference herein to a medicaldevice, or to an implant or a prosthesis having a sensor is to beunderstood to be a reference to an intelligent medical device orimplant/prosthesis wherein the sensor is located entirely within themedical device or implant/prosthesis. In embodiments of the presentdisclosure, reference herein to a medical device, or animplant/prosthesis having a sensor is to be understood to be a referenceto an intelligent medical device or implant/prosthesis wherein thesensor is one accelerometer or more than one accelerometer (e.g., two,three, four, five, six, seven, etc. accelerometers) located entirelywithin the medical device or implant/prosthesis. In embodiments of thepresent disclosure, reference herein to a medical device, or animplant/prosthesis having a sensor is to be understood to be a referenceto an intelligent medical device or implant/prosthesis wherein thesensor is one or more accelerometers (e.g., two, three, four, five, six,seven, etc. accelerometers) located entirely within a tibial extensionof the medical device, implant/prosthesis, such that the medical deviceor implant/prosthesis is, e.g. a component of a TKA.

The systems will include the intelligent implant and one or more of amemory that stores data from that detection and/or measuring, an antennathat transmits that data; a base station that receives the datagenerated by the sensor and may transmit the data and/or analyzed datato a cloud-based location; a cloud-based location where data may bestored and analyzed, and analyzed data may be stored and/or furtheranalyzed; and a receiving station that receives output from thecloud-based location, where that receiving station may be accessed,e.g., by a health care professional or an insurance company or themanufacturer of the implant, and the output may identify the status ofthe implant and/or the functioning of the implant and/or the status ofthe patient who has received the implant, and may also providerecommendations for addressing any concerns raised by analysis of theoriginal data.

For example, instability in the total joint arthroplasty (e.g. TKA, THAand TSA) hardware may lead to bone erosion and accelerated fatigue ofthe implant components. Left untreated or uncorrected, bone erosion andaccelerated fatigue will typically lead to pain and inflammation. By thetime pain and inflammation prompt a total joint arthroplasty (TJA)patient to seek medical care, the extent of bone erosion and TJA fatiguemay leave the health care professional with only one-choice: a highlyinvasive and expensive surgery with reduced probability of “successful”outcome. The present disclosure provides devices, systems and methodswhich provide that the instability in the TJA hardware can be detectedearly before bone erosion and implant fatigue damage. This instabilitycan be detected, quantified and characterized, and the resultscommunicated to a health care provider to allow for early treatmentand/or more effective treatment of the problem, i.e., the health careprovider may take advantage of corrective treatments that are far lessinvasive, less expensive, and more likely to succeed. The presentdisclosure also provides devices and/or methods to address theinstability problem.

The present disclosure refers to TJA (total joint arthroplasty) whichterm includes reference to the surgery and associated implanted hardwaresuch as a TJA prosthesis of the present disclosure. Features of methods,devices and systems of the present disclosure may be illustrated hereinby reference to a specific intelligent TJA prosthesis, however, thedisclosure should be understood to apply to any one or more TJAprosthesis, including a TKA (total knee arthroscopy) prosthesis, such asa TKI (total knee implant) which may also be referred to as a TKAsystem; a TSA (total shoulder arthroscopy) prosthesis, such as a TSI(total shoulder implant) which may also be referred to as a TSI system;and a THA (total hip arthroscopy) prosthesis, such as a THI (total hipimplant) which may also be referred to as a THA system. In oneembodiment the TJA prosthesis is an intelligent TJA, also referred to asan intelligent TJA prosthesis, having at least one sensor at disclosedherein.

This Brief Summary has been provided to introduce certain concepts in asimplified form that are further described in detail below in theDetailed Description. Except where otherwise expressly stated, thisBrief Summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to limit the scope of theclaimed subject matter.

The following are some exemplary numbered embodiments of the presentdisclosure:

1. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe medial side of the implant, as compared to the lateral side.

2. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe lateral side of the implant, as compared to the medial side.

3. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe anterior side of the implant, as compared to the posterior side.

4. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe posterior side of the implant, as compared to the anterior side.

5. A tibial insert/articular spacer/for an implantable knee prosthesis,comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mmthicker on the medial, lateral, anterior and/or posterior side of theimplant.

6. The tibial insert according to any one of embodiments 1-5, whereinsaid tibial insert is composed of polyethylene, or polyetheretherketone(PEEK).

7. The tibial insert according to any one of embodiments 1-6 whereinsaid tibial insert is customized to a patient.

8. The tibial insert according to any one of embodiments 1 to 7 whereinsaid insert is manufactured by 3-D printing, or, by molding.

9. An implantable medical device, comprising:

a circuit configured to be fixedly attached to an implantable prostheticdevice; a power component; and a device configured to uncouple thecircuit from the power component.

10. An implantable medical device, comprising: a circuit configured tobe fixedly attached to an implantable prosthetic device; a battery; anda fuse coupled between the circuit and the battery.

11. A method, comprising electrically opening a fuse that is disposedbetween a circuit and a battery, at least the fuse and the circuit beingdisposed on an implanted prosthetic device.

12. An implantable medical device, comprising: at least one sensorconfigured to generate a sensor signal; and a control circuit configuredto cause the at least one sensor to generate the sensor signal at afrequency that is related to a telemedicine code.

13. An implantable medical device, comprising: at least one sensorconfigured to generate a sensor signal; and a control circuit configuredto cause the at least one sensor to generate the sensor signal at afrequency that allows a doctor to qualify for payment under atelemedicine insurance code.

14. An implantable medical device, comprising: at least one sensorconfigured to generate a sensor signal; and a control circuit configuredto cause the at least one sensor to generate the sensor signal at afrequency that allows a doctor to qualify for full payment under atelemedicine insurance code.

15. A method, comprising, generating a sensor signal that is related toan implanted medical device at a frequency that allows a doctor toqualify for payment available under a telemedicine insurance code.

16. A method, comprising, generating a sensor signal that is related toan implanted medical device at a frequency that allows a doctor toqualify for full payment available under a telemedicine insurance code.

17. An implantable prosthesis, comprising:

-   -   a housing; and    -   an implantable circuit disposed in the housing and configured        -   to generate at least one first signal representative of a            movement;        -   to determine whether the signal meets at least one first            criterion; and        -   to send the signal to a remote location in response to            determining that the signal meets the at least one first            criterion.

18. A base station, comprising:

-   -   a housing; and    -   a base-station circuit disposed in the housing and configured        -   to receive, from an implantable prosthesis, at least first            signal representative of a movement;        -   to send the at least one first signal to a destination;        -   to receive at least one second signal from a source; and        -   to send the at least one second signal to the implantable            prosthesis.

19. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a current through the fuse exceeding an overcurrent threshold.

20. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a current through the fuse exceeding an overcurrent threshold for atleast a threshold time.

21. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a voltage across the fuse exceeding an overvoltage threshold.

22. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a voltage across the fuse exceeding an overvoltage threshold for atleast a threshold time.

23. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a temperature exceeds an overtemperature threshold.

24. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a temperature exceeding an overtemperature threshold for at least athreshold length of time.

25. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; andtransmitting the sensor signal to a remote location.

26. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; sampling thesensor signal; and transmitting the samples to a remote location.

27. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; determiningwhether the sensor signal represents a qualified event; and transmittingthe signal to a remote location in response to determining that thesensor signal represents a qualified event.

28. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; receiving apolling signal from a remote location; and transmitting the sensorsignal to the remote location in response to the polling signal.

29. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; generating amessage that includes the sensor signal or data representative of thesensor signal; and transmitting the message to a remote location.

30. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; generating adata packet that includes the sensor signal or data representative ofthe sensor signal; and transmitting the data packet to a remotelocation.

31. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; encrypting atleast a portion of the sensor signal or data representative of thesensor signal; and transmitting the encrypted sensor signal to a remotelocation.

32. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; encoding atleast a portion of the sensor signal or data representative of thesensor signal; and transmitting the encoded sensor signal to a remotelocation.

33. A method, comprising: generating a sensor signal in response to amovement of a subject in which a prosthesis is implanted; transmittingthe sensor signal to a remote location; and entering an implantablecircuit associated with the prosthesis into a lower-power mode aftertransmitting the sensor signal.

34. A method, comprising: generating a first sensor signal in responseto a movement of a subject in which a prosthesis is implanted;transmitting the first sensor signal to a remote location; entering atleast one component of an implantable circuit associated with theprosthesis into a lower-power mode after transmitting the sensor signal;and generating a second sensor signal in response to a movement of thesubject after an elapse of a low-power-mode time for which theimplantable circuit is configured.

35. A method, comprising: receiving a sensor signal from a prosthesisimplanted in a subject; and transmitting the received sensor signal to adestination.

36. A method, comprising: sending an inquiry to a prosthesis implantedin a subject, receiving a sensor signal from a prosthesis after sendingthe inquiry; and transmitting the received sensor signal to adestination.

37. A method, comprising: receiving a sensor signal and at least oneidentifier from a prosthesis implanted in a subject; determining whetherthe identifier is correct; and transmitting the received sensor signalto a destination in response to determining that the identifier iscorrect.

38. A method, comprising: receiving a message including a sensor signalfrom a prosthesis implanted in a subject; decrypting at least a portionof the message; and transmitting the decrypted message to a destination.

39. A method, comprising: receiving a message including a sensor signalfrom a prosthesis implanted in a subject; decoding at least a portion ofthe message; and transmitting the decoded message to a destination.

40. A method, comprising: receiving a message including a sensor signalfrom a prosthesis implanted in a subject; encoding at least a portion ofthe message; and transmitting the encoded message to a destination.

41. A method, comprising: receiving a message including a sensor signalfrom a prosthesis implanted in a subject; encrypting at least a portionof the message; and transmitting the encrypted message to a destination.

42. A method, comprising: receiving a data packet including a sensorsignal from a prosthesis implanted in a subject; decrypting at least aportion of the data packet; and transmitting the decrypted data packetto a destination.

43. A method, comprising: receiving a data packet including a sensorsignal from a prosthesis implanted in a subject; decoding at least aportion of the data packet; and transmitting the decoded data packet toa destination.

44. A method, comprising: receiving a data packet including a sensorsignal from a prosthesis implanted in a subject; encoding at least aportion of the data packet; and transmitting the encoded data packet toa destination.

45. A method, comprising: receiving a data packet including a sensorsignal from a prosthesis implanted in a subject; encrypting at least aportion of the data packet; and transmitting the encrypted data packetto a destination.

46. A method, comprising: receiving a sensor signal from a prosthesisimplanted in a subject; decrypting at least a portion of the sensorsignal; and transmitting the decrypted sensor signal to a destination.

47. A method, comprising: receiving a sensor signal from a prosthesisimplanted in a subject; decoding at least a portion of the sensorsignal; and transmitting the decoded sensor signal to a destination.

48. A method, comprising: receiving a sensor signal from a prosthesisimplanted in a subject; encoding at least a portion of the sensorsignal; and transmitting the encoded sensor signal to a destination.

49. A method, comprising: receiving a sensor signal from a prosthesisimplanted in a subject; encrypting at least a portion of the sensorsignal; and transmitting the encrypted sensor signal to a destination.

50. An implantable circuit for an implantable prosthesis.

51. An implantable prosthesis including an implantable circuit.

52. An implantable prosthesis including a fuse.

53. A base station for communication with an implantable prosthesis.

54. A monitoring-session-data collection, analysis, and status-reportingsystem implemented as a component of one or more computer systems, eachcomputer system having one or more processors, one or more memories, oneor more network connections, and access to one or more mass-storagedevices, the one or more the monitoring-session-data collection,data-analysis, and status-reporting system comprising:

-   -   a monitoring-session-data-receiving component that receives        monitoring-session-data, including acceleration data generated        by sensors within or proximal to a prosthesis attached or        implanted within a patient, from an external        monitoring-session-data source and that stores the received        monitoring-session-data in one or more of the one or more        memories and one or more mass-storage devices;    -   a monitoring-session-data-processing component that        -   prepares the monitoring-session-data for processing,        -   determines component trajectories representing motion modes            and additional metric values from the            monitoring-session-data; and    -   a monitoring-session-data-analysis component that        -   determines a prosthesis status and a patient status from the            motion modes and additional metric values,        -   distributes the determined prosthesis status and patient            status to target computer systems through the network            connections, and        -   when indicated by the determined prosthesis status and            patient status, distributes one or more alarms and events to            target computer systems through the network connections.

55. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data includes: a patient identifier; a deviceidentifier; a timestamp; device-configuration data; and an ordered setof data.

56. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 55 wherein the ordered set of datacomprises one of:

-   -   a time sequence of data vectors, each data vector including        numerical values related to linear-accelerations with respect to        three coordinate axes of an internal device coordinate system;        and    -   a time sequence of data vectors, each data vector including        numerical values related to linear-accelerations with respect to        three coordinate axes of a first internal device coordinate        system and including numerical values related to angular        velocities, numerical values related to angular velocities        relative to the first internal device coordinate system or to a        second internal device coordinate system.

57. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-processing component prepares themonitoring-session-data for processing by:

-   -   receiving a time sequence of data vectors, each data vector        including three numerical values related to linear-accelerations        in the directions of three coordinate axes of a first internal        device coordinate system and including three numerical values        related to angular velocities about each axis of the first or a        second internal device coordinate system;    -   when rescaling of the data-vector sequence is needed, rescaling        the numerical values of the data vectors;    -   when normalization of the data-vector sequence is needed,        normalizing the numerical values of the data vectors;    -   when transformation of one or more of the numerical values        related to linear-acceleration and the numerical values related        to angular velocities is needed to relate the numerical values        related to linear-acceleration and the numerical values related        to angular velocities to a common internal coordinate system,        transforming one or more of the numerical values related to        linear-acceleration and the numerical values related to angular        velocities to relate to the common internal coordinate system;        and    -   when the time sequence of data vectors needs to be synchronized        with respect to a fixed-interval time sequence, synchronizing        the data vectors with respect to a fixed-interval time sequence.

58. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-processing component determines componenttrajectories representing motion modes and additional metric values fromthe monitoring-session-data by:

-   -   orienting the prepared monitoring-session-data, comprising data        vectors, each data vector including three numerical values        related to linear-accelerations in the directions of three        coordinate axes of an internal device coordinate system and        including three numerical values related to angular velocities        about each axis of the internal device coordinate system, with        respect to a natural coordinate system;    -   bandpass filtering the oriented data vectors to obtain a set of        data vectors for each of multiple frequencies, including a        normal-motion frequency;    -   determining, from the data vectors for each of the        non-normal-motion frequencies, a spatial amplitude in each of        the coordinate-axis directions of the natural coordinate system;    -   determining, from a basis trajectory for the patient and the        data vectors for the normal-motion frequency, a spatial        amplitude in each of the coordinate-axis directions of the        natural coordinate system; and    -   determining, from the basis trajectory for the patient and the        data vectors for the normal-motion frequency, current        normal-motion characteristics.

59. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 58 wherein determining, from thedata vectors for a frequency, a spatial amplitude in each of thecoordinate-axis directions of the natural coordinate system furthercomprises: generating a spatial trajectory from the data vectors;projecting the spatial frequency onto each of the coordinate axes; anddetermining the lengths of the protections of the spatial frequency ontoeach of the coordinate axes.

60. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-analysis component determines a prosthesisstatus and a patient status from the motion modes and additional metricvalues by:

-   -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

61. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-analysis component wherein the one or morealarms and events distributed to target computer systems include:

-   -   an alarm that notifies a medical practitioner or medical        facility of the need, by the patient, of immediate assistance or        intervention; and    -   an event that indicates additional services and/or equipment        needed by the patient that may be handled by various external        computer systems to automatically provide the additional        services and/or equipment to the patient or inform the patient        of the additional services and/or equipment and provide the        patient with information regarding procurement of the additional        services and/or equipment.

62. A method, carried out by a monitoring-session-data collection,analysis, and status-reporting system implemented as a component of oneor more computer systems, each computer system having one or moreprocessors, one or more memories, one or more network connections, andaccess to one or more mass-storage devices, the method comprising:

-   -   receiving monitoring-session-data, including acceleration data        generated by sensors within or proximal to a prosthesis attached        or implanted within a patient, from an external        monitoring-session-data source;    -   storing the received monitoring-session-data in one or more of        the one or more memories and one or more mass-storage devices;    -   determining a prosthesis status and a patient status from the        motion modes and additional metric values,    -   distributing the determined prosthesis status and patient status        to target computer systems through the network connections, and    -   when indicated by the determined prosthesis status and patient        status, distributing one or more alarms and events to target        computer systems through the network connections.

63. The method of embodiment 62 wherein determining a prosthesis statusand a patient status from the motion modes and additional metric valuesfurther comprises:

-   -   preparing the monitoring-session-data for processing,    -   determines component trajectories representing motion modes and        additional metric values from the monitoring-session-data;    -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

64. The method of embodiment 62 wherein preparing themonitoring-session-data for processing further comprises

-   -   receiving a time sequence of data vectors, each data vector        including three numerical values related to linear-accelerations        in the directions of three coordinate axes of a first internal        device coordinate system and including three numerical values        related to angular velocities about each axis of the first or a        second internal device coordinate system;    -   when rescaling of the data-vector sequence is needed, rescaling        the numerical values of the data vectors;    -   when normalization of the data-vector sequence is needed,        normalizing the numerical values of the data vectors;    -   when transformation of one or more of the numerical values        related to linear-acceleration and the numerical values related        to angular velocities is needed to relate the numerical values        related to linear-acceleration and the numerical values related        to angular velocities to a common internal coordinate system,        transforming one or more of the numerical values related to        linear-acceleration and the numerical values related to angular        velocities to relate to the common internal coordinate system;        and    -   when the time sequence of data vectors needs to be synchronized        with respect to a fixed-interval time sequence, synchronizing        the data vectors with respect to a fixed-interval time sequence.

65. The method of embodiment 62 wherein determining componenttrajectories representing motion modes and additional metric values fromthe monitoring-session-data by:

-   -   orienting the prepared monitoring-session-data, comprising data        vectors, each data vector including three numerical values        related to linear-accelerations in the directions of three        coordinate axes of an internal device coordinate system and        including three numerical values related to angular velocities        about each axis of the internal device coordinate system, with        respect to a natural coordinate system;    -   bandpass filtering the oriented data vectors to obtain a set of        data vectors for each of multiple frequencies, including a        normal-motion frequency;    -   determining, from the data vectors for each of the        non-normal-motion frequencies, a spatial amplitude in each of        the coordinate-axis directions of the natural coordinate system;    -   determining, from a basis trajectory for the patient and the        data vectors for the normal-motion frequency, a spatial        amplitude in each of the coordinate-axis directions of the        natural coordinate system; and    -   determining, from the basis trajectory for the patient and the        data vectors for the normal-motion frequency, current        normal-motion characteristics.

66. The method of embodiment 54 wherein determining, from the datavectors for a frequency, a spatial amplitude in each of thecoordinate-axis directions of the natural coordinate system furthercomprises:

-   -   generating a spatial trajectory from the data vectors;    -   projecting the spatial frequency onto each of the coordinate        axes; and    -   determining the lengths of the protections of the spatial        frequency onto each of the coordinate axes.

67. The method of embodiment 54 wherein determining a prosthesis statusand a patient status from the motion modes and additional metric valuesfurther comprises:

-   -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

68. The method of embodiment 54 wherein the one or more alarms andevents distributed to target computer systems include:

-   -   an alarm that notifies a medical practitioner or medical        facility of the need, by the patient, of immediate assistance or        intervention; and    -   an event that indicates additional services and/or equipment        needed by the patient that may be handled by various external        computer systems to automatically provide the additional        services and/or equipment to the patient or inform the patient        of the additional services and/or equipment and provide the        patient with information regarding procurement of the additional        services and/or equipment.

69. A physical data-storage device encoded with computer instructionsthat, when executed by one or more processors within one or morecomputer systems of a monitoring-session-data collection, analysis, andstatus-reporting system, each computer system having one or moreprocessors, one or more memories, one or more network connections, andaccess to one or more mass-storage devices, control themonitoring-session-data collection, analysis, and status-reportingsystem to:

-   -   receive monitoring-session-data, including acceleration data        generated by sensors within or proximal to a prosthesis attached        or implanted within a patient, from an external        monitoring-session-data source.

70. A method for determining joint loosening in a patient having animplanted artificial joint, comprising a) analyzing movement of animplanted artificial joint, and b) comparing said movement vs.previous/standardized norms.

71. A method for determining loosening of an implanted prosthesis in apatient having the implanted prosthesis, comprising:

-   -   a. obtaining a standardized norm of movement by analyzing        movement of an implanted prosthesis during one or more first        monitoring sessions,    -   b. obtaining a current description of movement by analyzing        movement of an implanted prosthesis during one or more second        monitoring sessions that occur subsequent to the one or more        first monitoring sessions; and    -   c. comparing said current description of movement to said        standardized norm of movement, to thereby identify loosening of        an implanted prothesis in a patient having the implanted        prosthesis.

72. A method for identifying a clinical or subclinical conditionassociated with an implant in a patient, the method comprising:

-   -   a. monitoring a first motion of the implant during a first        monitoring session using a sensor which is directly coupled to        the implant, to provide a first monitoring-session data for the        first motion;    -   b. monitoring a second motion of the implant during a second        monitoring session using the sensor, to provide a second        monitoring-session-data for the second motion; and    -   c. comparing the first monitoring-session data or a product        thereof to the second monitoring-session-data or a product        thereof, to provide a comparison that is indicative of a        clinical or subclinical condition associated with the implant.

73. The method of embodiment 72 wherein the clinical or subclinicalcondition is a loosening of the implant (motion of prosthesis within thesurrounding bone or cement, e.g., the implant becomes separated from thehost bone due, e.g., to periprosthetic lucency or periprostheticosteolysis).

74. The method of embodiment 72 wherein the clinical or subclinicalcondition is a malalignment (sub-optimal positioning of a prostheticcomponent) or a realignment of the implant (change in alignment ofprosthetic component).

75. The method of embodiment 72 wherein the clinical or subclinicalcondition is deformation (wear) of the implant.

76. The method of embodiment 72 wherein the patient is asymptomatic forthe condition, and the comparison of the first and second data orproducts thereof indicate that the condition has occurred between thefirst and second monitoring sessions.

77. The method of embodiment 72 wherein the patient is asymptomatic forloosening of the implant, and the comparison of the first and seconddata or products thereof indicate that the implant has loosened betweenthe first and second monitoring sessions.

78. The method of embodiment 72 wherein the patient is asymptomatic forrealignment of the implant, and comparison of the first and second dataor products thereof indicate that the implant has changed alignmentbetween the first and second monitoring sessions.

79. The method of embodiment 72 wherein the patient is asymptomatic fordeformation of the implant, and comparison of the first and second dataor products thereof indicate that the implant has deformed between thefirst and second monitoring sessions.

80. A method for treating a clinical or subclinical condition associatedwith an implant in a patient, comprising:

-   -   a. identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b. attaching corrective external bracing to patient to restore        proper alignment and/or enhanced stability to the implant.

81. The method of embodiment 80 wherein the corrective external bracinghas been specifically tailored to the patient and the subclinicalcondition.

82. A method for treating a clinical or subclinical condition associatedwith an implant in a patient, comprising:

-   -   a. identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b. contacting the implant with a fixation system to retard        progression of the subclinical condition.

83. The method of embodiment 82 wherein the fixation system compriseshardware selected from a K-wire, pin, screw, plate and intramedullarydevice.

84. The method of embodiment 82 wherein a screw is located through abone that holds the implant, where a terminus of the screw pushesagainst a surface of the implant to retard movement of the implant,where a screw is selected from one, two, three, four, five, six, seven,eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen,seventeen, eighteen, nineteen and twenty screws.

85. The method of embodiment 82 wherein the fixation system comprisesbone cement.

86. A method for treating a clinical or subclinical condition associatedwith an implant in a patient, comprising:

-   -   a. identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b. contacting the implant with a tamp, where the contacting        changes a location of the implant within the patient; and        optionally    -   c. applying a cement around the implant having the changed        location.

87. The method of embodiment 86 wherein the subclinical condition is arealignment of the implant.

88. A method for treating a clinical or subclinical condition associatedwith an implant in a patient, comprising:

-   -   a. identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b. implanting an insert adjacent to a component of the implant,        where the insert modifies forces acting on the component of the        implant.

89. The method of embodiment 88 wherein the insert is a tibial insert.

90. The method of embodiment 88 wherein the insert is a tibial inserthaving (i) a lateral side with a minimum thickness and (ii) a medialside with a minimum thickness that is non-identical to the minimumthickness of the lateral side.

91. A method for treating a clinical or subclinical condition associatedwith an implant in a patient, comprising:

-   -   a. identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b. delivering a pro-osteointegration agent to a location        surrounding the implant.

92. The method of embodiment 91 wherein the pro-osteointegration agentis selected from autologous bone graft, xenograph bone graft, syntheticbone graft, bone pastes, bone growth factor, and growth factor.

93. A method for treating a clinical or subclinical condition associatedwith an implant in a patient, comprising:

-   -   a. identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b. delivering an anti-bacterial agent to a location surrounding        the implant.

94. The method of embodiment 93 wherein the anti-bacterial agent iscompounded in a sustained release form.

95. The method of any of embodiments 72-94 wherein the implant is anintelligent implant.

96. The method of embodiments 72-94 wherein the implant is selected froma knee implant, a hip implant and a shoulder implant.

97. The method of any of embodiments 72-94 wherein the product of themonitoring-session data comprises a motion mode.

98. The method of any of embodiments 72-94 wherein the product of themonitoring-session data comprises a motion mode, and a status of theimplant is determined from the motion mode.

99. The method of any of embodiments 72-94 wherein the product of themonitoring-session data comprises a motion mode, and a status of thepatient is determined from the motion mode.

100. The method of any of embodiments 72-94 wherein the implant has beenlocated within the patient for at least 10 weeks prior to the firstmonitoring session.

101. The method of any of embodiments 72-94 wherein the implant haschanged alignment over a period of at least 2 weeks.

102. The method of any of embodiments 72-94 wherein the implant hasloosened over a period of at least two weeks.

103. The method of any of embodiments 72-94 wherein the implant hasdeformed over a period of at least two weeks.

104. The method of any of embodiments 72-94 wherein the implantcomprises a control circuit configured to cause the sensor to generate asensor signal at a frequency that is related to a telemedicine code forthe clinical or subclinical condition, and the sensor signal isgenerated at the frequency.

105. The method of any of embodiments 72-94 wherein the implantcomprises a control circuit configured to cause the sensor to generate asensor signal at a frequency that allows a doctor to qualify for paymentunder a telemedicine insurance code, and the sensor signal is generatedat the frequency.

106. The method of any of embodiments 72-94 wherein the implantcomprises a control circuit configured to cause the sensor to generate asensor signal at a frequency that allows a doctor to qualify for fullpayment under a telemedicine insurance code, and the sensor signal isgenerated at the frequency.

107. The method of any of embodiments 72-94 further comprisinggenerating a sensor signal that is related to the implant at a frequencythat allows (i) a doctor to qualify for full payment available under atelemedicine insurance code, or (ii) a doctor to qualify for paymentavailable under a telemedicine insurance code.

108. A method comprising:

-   -   a. providing an intelligent prosthesis implanted in a bone        adjacent to a joint of a patient, where an accelerometer is        contained within the intelligent prosthesis, and where the        accelerometer is positioned within the bone;    -   b. moving the implanted intelligent prosthesis relative to an        external environment wherein the patient is located, where the        implanted intelligent prosthesis is moved during a first        monitoring session;    -   c. making first measurements with the accelerometer during the        first monitoring session, where the first measurements provide        first monitoring-session-data or a product thereof which        identifies a status of the implanted intelligent prosthesis at a        time of the first measurements.

109. The method of embodiment 108 wherein the accelerometer is aplurality of accelerometers.

110. The method of embodiment 108 wherein the accelerometer is selectedfrom a 1-axis accelerometer, a 2-axis accelerometer and a 3-axisaccelerometer.

111. The method of embodiment 108 wherein the accelerometer operates ina broadband mode.

112. The method of embodiment 108 wherein the bone is a tibia.

113. The method of embodiment 108 wherein the accelerometer is locatedin a tibial extension of the intelligent prosthesis.

114. The method of embodiment 108 wherein the implanted intelligentprosthesis is moved relative to the external environment without animpact force being applied to the patient or the intelligent prosthesisduring the first monitoring session.

115. The method of embodiment 108 wherein the external environmentcomprises a residence of the patient.

116. The method of embodiment 108 wherein the external environmentcomprises an operating room wherein the intelligent prosthesis has beenimplanted into the patient.

117. The method of embodiment 108 wherein the status of the implantedintelligent prosthesis is a characterization of the looseness of theimplanted intelligent prosthesis within the bone.

118. The method of embodiment 108 wherein the status of the implantedintelligent prosthesis is a characterization of the alignment of theimplanted intelligent prosthesis within the bone.

119. The method of embodiment 108 wherein the status of the implantedintelligent prosthesis is a characterization of the wear of theimplanted intelligent prosthesis.

120. The method of embodiment 108 wherein the status of the implantedintelligent prosthesis is a characterization of bacterial infection of aregion within the bone adjacent to the implanted intelligent prosthesis.

121. The method of embodiment 108 wherein the status of the implantedintelligent prosthesis indicates a subclinical condition.

122. The method of embodiment 108 wherein step b) is repeated after awaiting period, where the repeat of step b) comprises moving theimplanted intelligent prosthesis relative to an external environmentwherein the patient is located, where the implanted intelligentprosthesis is moved during a second monitoring session, and whereinsecond measurements are made with the accelerometer during the secondmonitoring session, where the second measurements provide secondmonitoring-session-data or a product thereof which identifies a statusof the implanted of the implanted intelligent prosthesis at the time ofthe second measurements.

123. The method of embodiment 108 wherein step b) is repeated aplurality of times, the plurality of times separated from one another byidentical or non-identical waiting periods, where the repeating of stepb) comprises moving the implanted intelligent prosthesis relative to anexternal environment wherein the patient is located, where the implantedintelligent prosthesis is moved during a plurality of monitoringsessions, and wherein measurements are made with the accelerometerduring each of the plurality of monitoring sessions, where themeasurements provide a plurality of monitoring-session-data or productsthereof, each of which identifies a status of the implanted of theimplanted intelligent prosthesis at the time of the measurements.

124. The method of embodiment 108 wherein step b) is repeated aplurality of times, the plurality of times separated from one another byidentical or non-identical waiting periods, where the repeating of stepb) comprises moving the implanted intelligent prosthesis relative to anexternal environment wherein the patient is located, where the implantedintelligent prosthesis is moved during a plurality of monitoringsessions, and wherein measurements are made with the accelerometerduring each of the plurality of monitoring sessions, where themeasurements provide a plurality of monitoring-session-data or productsthereof, each of which identifies a status of the implanted of theimplanted intelligent prosthesis at the time of the measurements;wherein the plurality is optionally selected from 2 to 20 monitoringsessions, and where the plurality of monitoring-session data takentogether indicate a change in the status of the implanted intelligentprosthesis during the time when the plurality of monitoring sessionsoccurred.

125. The method of embodiment 124 wherein the change in the status isindicative of a healing of the tissue surrounding the implantedintelligent prosthesis.

126. The method of embodiment 124 wherein the change in the status isindicative of an infection of the tissue surrounding the implantedprosthesis.

127. The method of embodiment 124 wherein the change in the status isindicative of a loosening of the implanted intelligent prothesis withinthe bone.

128. The method of embodiment 124 wherein the change in status isindicative of wear of the implanted intelligent prosthesis.

129. The method of embodiment 124 wherein the change in status isindicative of malalignment of the implanted intelligent prosthesis.

130. The method of embodiment 124 wherein the change in status isindicative of a change in alignment of the implanted intelligentprosthesis.

The details of one or more embodiments are set forth in the descriptionbelow. The features illustrated or described in connection with oneexemplary embodiment may be combined with the features of otherembodiments. Thus, any of the various embodiments described herein canbe combined to provide further embodiments. Aspects of the embodimentscan be modified, if necessary to employ concepts of the various patents,applications and publications as identified herein to provide yetfurther embodiments. Other features, objects and advantages will beapparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary features of the present disclosure, its nature and variousadvantages will be apparent from the accompanying drawings and thefollowing detailed description of various embodiments. Non-limiting andnon-exhaustive embodiments are described with reference to theaccompanying drawings, wherein like labels or reference numbers refer tolike parts throughout the various views unless otherwise specified. Thesizes and relative positions of elements in the drawings are notnecessarily drawn to scale. For example, the shapes of various elementsare selected, enlarged, and positioned to improve drawing legibility.The particular shapes of the elements as drawn have been selected forease of recognition in the drawings. One or more embodiments aredescribed hereinafter with reference to the accompanying drawings inwhich:

FIG. 1 illustrates an exemplary intelligent implant.

FIG. 2 illustrates including the implant of FIG. 1 as part of a jointprosthesis, and locating that prosthesis in a tibia.

FIG. 3 is a context diagram of a kinematic implantable deviceenvironment in a patient's home, according to an embodiment.

FIG. 4 is a block diagram of an implantable circuit for an implantableprosthesis, such as an implantable knee prosthesis, where the circuitincludes an implantable reporting processor (IRP), according to anembodiment.

FIG. 5 is a block diagram of a base-station circuit for a base stationconfigured to communicate with the implantable circuit of FIG. 101, andto forward data from the implantable circuit to a remote processingserver such as a cloud-based server.

FIG. 6 is a perspective view of an inertial measurement unit (IMU) ofthe implantable circuit of FIG. 4 and of a set of coordinate axes withinthe frame of reference of the IMU, according to an embodiment.

FIG. 7 is a front view of a standing patient in which a knee prosthesisis implanted and of two of the axes of the IMU of FIG. 6, according toan embodiment.

FIG. 8 is a side view of the patient of FIG. 7 in a supine position andof two of the axes of the IMU of FIG. 6, according to an embodiment.

FIG. 9 is a plot, versus time, of the accelerations measured along thex, y, and z axes of the IMU of FIG. 6 while the patient of FIGS. 7 and 8is walking with a normal gait, according to an embodiment.

FIG. 10 is a plot, versus time, of the angular velocities measured aboutthe x, y, and z axes of the IMU of FIG. 6 while the patient of FIGS. 7and 8 is walking with a normal gait, according to an embodiment.

FIG. 11 is a plot, versus time, of a time-scale-expanded portion of theplot of FIG. 9, according to an embodiment.

FIG. 12 is a plot, versus time, of a time-scale-expanded portion of theplot of FIG. 10, according to an embodiment.

FIG. 13 is a plot, versus time, of the accelerations measured along thex, y, and z axes of the IMU of FIG. 6 during impact of the heel of thepatient of FIGS. 7 and 8 while the patient is walking with a normalgait, according to an embodiment.

FIG. 14 is a plot, versus frequency, of the respective spectraldistribution of each of the x, y, and z accelerations of FIG. 13,according to an embodiment.

FIG. 15 is a plot, versus frequency, of the cumulative spectraldistribution of the x, y, and z accelerations of FIG. 13, according toan embodiment.

FIG. 16 is a plot, versus time, of the accelerations measured along thex, y, and z axes of the IMU of FIG. 6 during impact of a heel of thepatient of FIGS. 7 and 8 while the patient is walking with a normal gaitand while a knee prosthesis implanted in the patient exhibits aninstability, according to an embodiment.

FIG. 17 is a plot, versus frequency, of the respective spectraldistribution of each of the x, y, and z accelerations of FIG. 16,according to an embodiment.

FIG. 18 is a plot, versus frequency, of the cumulative spectraldistribution of the x, y, and z accelerations of FIG. 16, according toan embodiment.

FIG. 19 is a plot, versus time, of the accelerations measured along thex, y, and z axes of the IMU of FIG. 6 during impact of a heel of thepatient of FIGS. 7 and 8 while the patient is walking with a normal gaitand while a knee prosthesis implanted in the patient exhibits aninstability and early-onset degradation, according to an embodiment.

FIG. 20 is a plot, versus frequency, of the respective spectraldistribution of each of the x, y, and z accelerations of FIG. 19,according to an embodiment.

FIG. 21 is a plot, versus frequency, of the cumulative spectraldistribution of the x, y, and z accelerations of FIG. 19, according toan embodiment.

FIG. 22 is a plot, versus time, of the accelerations measured along thex, y, and z axes of the IMU of FIG. 6 during impact of a heel of thepatient of FIGS. 7 and 8 while the patient is walking with a normal gaitand while a knee prosthesis implanted in the patient exhibits aninstability and advanced degradation, according to an embodiment.

FIG. 23 is a plot, versus frequency, of the respective spectraldistribution of each of the x, y, and z accelerations of FIG. 22,according to an embodiment.

FIG. 24 is a plot, versus frequency, of the cumulative spectraldistribution of the x, y, and z accelerations of FIG. 22, according toan embodiment.

FIG. 25 is a flow diagram of operation of the implantable circuitry ofFIG. 4, according to an embodiment.

FIG. 26 is a flow diagram of operation of the base-station circuitry ofFIG. 5, according to an embodiment.

FIG. 27 is a flow diagram of operation of the fuse of FIG. 4, accordingto an embodiment.

FIG. 28 illustrates a three-dimensional Cartesian coordinate space andthe representation of a point in the space by a vector.

FIG. 29A and FIG. 29B each illustrate the data output by an IMU.

FIG. 30A, FIG. 30B, FIG. 30C, FIG. 30D, FIG. 30E, FIG. 30F and FIG. 30Geach illustrate complex space curves that represent motions andresolution of the complex space curves into component motions.

FIG. 31 illustrates one method for dealing with types of non-periodicmotions.

FIG. 32A, FIG. 32B, FIG. 32C, FIG. 32D, FIG. 32E and FIG. 32F eachillustrate the principle-component-analysis method that is used torotate an initial coordinate system to a coordinate system in which theaxes are aligned with the distributions of points representingexperimental observations.

FIG. 33 illustrates use of principal component analysis to determine thenatural coordinate system based on raw or filtered IMU output data.

FIG. 34A, FIG. 34B, FIG. 34C and FIG. 34D each illustrate forward andinverse Fourier transforms.

FIG. 35 illustrates the use of Fourier transforms on the data-vectoroutput of the IMU.

FIG. 36A and FIG. 36B each illustrate the data output by thedata-processing application as a result of processing and analyzing theraw data, obtained during a monitoring session that is received from abase station.

FIG. 36C illustrates a final portion of the results generated by thedata processing application as a result of processing and analyzing theraw data, obtained during a monitoring session that is received from abase station.

FIG. 37A, FIG. 37B, FIG. 37C, FIG. 37D, FIG. 37E, FIG. 37F, FIG. 37G,and FIG. 37H each provide control-flow diagrams that illustrate thecurrently discussed implementation of the data-processing applicationthat processes patient-monitoring-session data.

FIG. 38A illustrates representative cloud-based systems and methods forgenerating and processing data, communication pathways, reportgeneration and revenue generation. FIG. 38B illustrates representativelocal based systems and methods for generating and processing data,communication pathways, report generation and revenue generation.

FIG. 39 illustrates components of a currently used total kneearthroscopy system (3010), specifically a femoral component (3012), atibial insert (3014) and a tibial component (3016), where the tibialcomponent (3016) includes a tibial plate (3018) and a tibial stem(3020).

FIG. 40A, FIG. 40B, FIG. 40C and FIG. 40D illustrate exemplary tibialcomponents.

FIG. 41 illustrates a tibial insert.

FIG. 42A illustrates a cross-sectional view of the tibial insert of FIG.41.

FIG. 42B illustrates a deviation in the cross-sectional view of FIG.42A.

FIG. 43 illustrates a tibial insert.

FIG. 44A illustrates a cross-sectional view of the tibial insert of FIG.43.

FIG. 44B illustrates a deviation in the cross-sectional view of the FIG.44A.

FIG. 45 illustrates a tibial insert with a horn that extends into afemoral component.

FIG. 46 illustrates a tibial insert with a spike that extends into atibial component.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure may be understood more readily by reference tothe following detailed description of preferred embodiments of thedisclosure and the Examples of “intelligent prosthesis” included herein.The following description, along with the accompanying drawings, setsforth certain specific details in order to provide a thoroughunderstanding of various disclosed embodiments. However, one skilled inthe relevant art will recognize that the disclosed embodiments may bepracticed in various combinations, without one or more of these specificdetails, or with other methods, components, devices, materials, etc. Inother instances, well-known structures or components that are associatedwith the environment of the present disclosure, including but notlimited to the communication systems and networks, have not been shownor described in order to avoid unnecessarily obscuring descriptions ofthe embodiments. Additionally, the various embodiments may be methods,systems, media, or devices. Accordingly, the various embodiments may beentirely hardware embodiments, entirely software embodiments, entirelyfirmware embodiments, or embodiments combining or subcombining software,firmware, and hardware aspects.

Prior to setting forth this disclosure in more detail, it may be helpfulto an understanding thereof to provide definitions of certain terms tobe used herein. Additional definitions are set forth throughout thisdisclosure. The terms “include” and “comprise,” as well as derivativesthereof, mean inclusion without limitation. The term “or,” is inclusive,meaning and/or. The phrases “associated with” and “associatedtherewith,” as well as derivatives thereof, may mean to include, beincluded within, interconnect with, contain, be contained within,connect to or with, couple to or with, be communicable with, cooperatewith, interleave, juxtapose, be proximate to, be bound to or with, have,have a property of, or the like. The term “controller” means any device,system, or part thereof that controls at least one operation, such adevice may be implemented in hardware (e.g., electronic circuitry),firmware, or software, or some combination of at least two of the same.The functionality associated with any particular controller may becentralized or distributed, whether locally or remotely. Otherdefinitions of certain words and phrases may be provided within thispatent document. Those of ordinary skill in the art will understand thatin many, if not most instances, such definitions apply to prior as wellas future uses of such defined words and phrases.

An “intelligent prosthesis” or “intelligent medical device” as used inthe present disclosure, is an implantable or implanted medical devicethat desirably replaces or functionally supplements a subject's naturalbody part. As used herein, the term “intelligent prosthesis” isinterchangeably referred to as an “intelligent implant,” a “smartimplant,” a “smart medical device,” a “joint implant” an “implantedmedical device”, or by another like term. When the intelligentprosthesis makes kinematic measurements, it may be referred to as a“kinematic medical device,” or a “kinematic implantable device”. Indescribing embodiments of the present disclosure, reference may be madeto a kinematic implantable device, however it should be understood thatthis is exemplary only of the intelligent medical devices which may beemployed in the devices, methods, systems etc. of the presentdisclosure. Whether or not the intelligent prosthesis makes kinematic,or makes other or additional measurements, the prosthesis will compriseor be associated with an implantable reporting processor (IRP). In oneembodiment, the intelligent prosthesis is an implanted or implantablemedical device having an implantable reporting processor arranged toperform the functions as described herein. The intelligent prosthesismay perform one or more of the following exemplary actions in order tocharacterize the post-implantation status of the intelligent prosthesis:identifying the intelligent prosthesis or a portion of the intelligentprosthesis, e.g., by recognizing one or more unique identification codesfor the intelligent prosthesis or a portion of the intelligentprosthesis; detecting, sensing and/or measuring parameters, which maycollectively be referred to as monitoring parameters, in order tocollect operational, kinematic, or other data about the intelligentprosthesis or a portion of the intelligent prosthesis and wherein suchdata may optionally be collected as a function of time; storing thecollected data within the intelligent prosthesis or a portion of theintelligent prosthesis; and communicating the collected data and/or thestored data by a wireless means from the intelligent prosthesis or aportion of the intelligent prosthesis to an external computing device.The external computing device may have or otherwise have access to atleast one data storage location such as found on a personal computer, abase station, a computer network, a cloud-based storage system, oranother computing device that has access to such storage. Non-limitingand non-exhaustive list of embodiments of intelligent prostheses includetotal joint arthroplasty such as total knee arthroplasty (TKA), a TKAtibial plate, a TKA femoral component, a TKA patellar component, atibial extension, a total hip arthroplasty (THA), a femoral componentfor a THA, the acetabular component for a THA, a shoulder arthroplasty,a breast implant, an intramedullary rod for arm or leg breakage repair,a scoliosis rod, a dynamic hip screw, a spinal interbody spacer, aspinal artificial disc, an annuloplasty ring, a heart valve, anintravascular stent, a vascular graft and a vascular stent graft.

“Kinematic data,” as used herein, individually or collectively includessome or all data associated with a particular kinematic implantabledevice and available for communication outside of the particularkinematic implantable device. For example, kinematic data may includeraw data from one or more sensors of a kinematic implantable device,wherein the one or more sensors include such as gyroscopes,accelerometers, pedometers, strain gauges, and the like that producedata associated with motion, force, tension, velocity, or othermechanical forces. Kinematic data may also include processed data fromone or more sensors, status data, operational data, control data, faultdata, time data, scheduled data, event data, log data, and the likeassociated with the particular kinematic implantable device. In somecases, high resolution kinematic data includes kinematic data from one,many, or all of the sensors of the kinematic implantable device that iscollected in higher quantities, resolution, from more sensors, morefrequently, or the like.

In one embodiment, kinematics refers to the measurement of thepositions, angles, velocities, and accelerations of body segments andjoints during motion. Body segments are considered to be rigid bodiesfor the purposes of describing the motion of the body. They include thefoot, shank (leg), thigh, pelvis, thorax, hand, forearm, upper-arm andhead. Joints between adjacent segments include the ankle (talocruralplus subtalar joints), knee, hip, wrist, elbow and shoulder. Positiondescribes the location of a body segment or joint in space, measured interms of distance, e.g., in meters. A related measurement calleddisplacement refers to the position with respect to a starting position.In two dimensions, the position is given in Cartesian co-ordinates, withhorizontal followed by vertical position. In one embodiment, a kinematicimplant or intelligent kinematic implants obtains kinematic data, andoptionally only obtains only kinematic data.

“Sensor” refers to a device that can be utilized to do one or more ofdetect, measure and/or monitor one or more different aspects of a bodytissue (anatomy, physiology, metabolism, and/or function) and/or one ormore aspects of the orthopedic device or implant. Representativeexamples of sensors suitable for use within the present disclosureinclude, for example, fluid pressure sensors, fluid volume sensors,contact sensors, position sensors, pulse pressure sensors, blood volumesensors, blood flow sensors, chemistry sensors (e.g., for blood and/orother fluids), metabolic sensors (e.g., for blood and/or other fluids),accelerometers, mechanical stress sensors and temperature sensors.Within certain embodiments the sensor can be a wireless sensor, or,within other embodiments, a sensor connected to a wirelessmicroprocessor. Within further embodiments one or more (including all)of the sensors can have a Unique Sensor Identification number (“USI”)which specifically identifies the sensor. In certain embodiments, thesensor is a device that can be utilized to measure in a quantitativemanner, one or more different aspects of a body tissue (anatomy,physiology, metabolism, and/or function) and/or one or more aspects ofthe orthopedic device or implant. In certain embodiments, the sensor isan accelerometer that can be utilized to measure in a quantitativemanner, one or more different aspects of a body tissue (e.g., function)and/or one or more aspects of the orthopedic device or implant (e.g.,alignment in the patient).

A wide variety of sensors (also referred to as MicroelectromechanicalSystems or “MEMS”, or Nanoelectromechanical Systems or “NEMS”, andBioMEMS or BioNEMS, see generally https://en.wikipedia.org/wiki/MEMS)can be utilized within the present disclosure. Representative patentsand patent applications include U.S. Pat. Nos. 7,383,071, 7,450,332;7,463,997, 7,924,267 and 8,634,928, and U.S. Publication Nos.2010/0285082, and 2013/0215979. Representative publications include“Introduction to BioMEMS” by Albert Foch, CRC Press, 2013; “From MEMS toBio-MEMS and Bio-NEMS: Manufacturing Techniques and Applications by MarcJ. Madou, CRC Press 2011; “Bio-MEMS: Science and EngineeringPerspectives, by Simona Badilescu, CRC Press 2011; “Fundamentals ofBioMEMS and Medical Microdevices” by Steven S. Saliterman, SPIE—TheInternational Society of Optical Engineering, 2006; “Bio-MEMS:Technologies and Applications”, edited by Wanjun Wang and Steven A.Soper, CRC Press, 2012; and “Inertial MEMS: Principles and Practice” byVolker Kempe, Cambridge University Press, 2011; Polla, D. L., et al.,“Microdevices in Medicine,” Ann. Rev. Biomed. Eng. 2000, 02:551-576;Yun, K. S., et al., “A Surface-Tension Driven Micropump for Low-voltageand Low-Power Operations,” J. Microelectromechanical Sys., 11:5, October2002, 454-461; Yeh, R., et al., “Single Mask, Large Force, and LargeDisplacement Electrostatic Linear Inchworm Motors,” J.Microelectromechanical Sys., 11:4, August 2002, 330-336; and Loh, N. C.,et al., “Sub-10 cm3 Interferometric Accelerometer with Nano-gResolution,” J. Microelectromechanical Sys., 11:3, June 2002, 182-187;all of the above of which are incorporated by reference in theirentirety.

In order to further understand the various aspects of the embodiments ofthe present disclosure provided herein, the following sections areprovided below: A. Intelligent Medical Devices and Implants; B. Systemswith Intelligent Implants; C. Joint Implant and Systems with JointImplant; D. Computer Systems for Analysis, Dissemination of Information,Ordering, and Supply: Processing IMU Data Recorded During PatientMonitoring; E. Methods and Devices for Stabilizing an Artificial Joint;F. Methods and Devices for Adjusting Position of an Artificial Joint; G.Joint Inserts and Use Thereof; and H. Clinical Solutions and Products.

A. Intelligent Medical Devices and Implants

In one aspect, the present disclosure provides medical devices,including medical devices which may be implanted into a patient(implants), which may be utilized to monitor and report the statusand/or activities of the medical device, including post-surgicalactivities and progress of the patient involved, as well as featuresthereof. In one embodiment, the present disclosure provides anintelligent implant that achieves the benefit of a medical implant,e.g., the benefit afforded by a prosthesis which replaces or supplementsa natural function of a patient, while also achieving the benefit ofmonitoring and reporting, which provides insight into the functionand/or condition of the device and/or the patient who has received theimplanted device. In one embodiment, the medical device is animplantable device that is an in vivo implantable prosthesis that can beimplanted into the body of a living host (also referred to as apatient), for example, to improve the function of, or to replace, abiological structure or organ of the patient's body.

In one embodiment of the present disclosure, the medical implant is astent graft and the intelligent implant is a stent graft coupled to asensor, e.g., as disclosed in PCT Publication No. WO 2014/100795 andU.S. Pat. No. 9,949,692, as well as PCT Publication No. WO 2016/044651and U.S. Patent Publ. No. 20160310077.

In one embodiment of the present disclosure, the medical implant is astent and the intelligent implant is a stent coupled to a sensor, e.g.,a stent monitoring assembly as disclosed in PCT Publication No. WO2014/144070 and U.S. Patent Publ. No. 2016/0038087, as well as PCTPublication No. WO 2016/044651 and U.S. Patent Publ. No. 20160310077.

In one embodiment of the present disclosure, the medical implant is ahip replacement prosthesis including one or more of a femoral stem,femoral head and an acetabular implant, and the intelligent implant is asensor coupled to the hip replacement prosthesis or a component thereof,e.g., a hip replacement as disclosed in PCT Publication No. WO2014/144107 and U.S. Patent Publ. No. 2016/0029952, as well as PCTPublication No. WO 2016/044651 and U.S. Patent Publ. No. 20160310077.

In one embodiment of the present disclosure, the medical implant is amedical tube, and the intelligent implant is a medical tube coupled to asensor. Medical tube refers to a generally cylindrical body which can beused in a medical procedure (e.g., the tubes are generally sterile,non-pyrogenic, and/or suitable for use and/or implantation into humans).For example, tubes can be utilized to: 1) bypass an obstruction (e.g.,in the case of Coronary Artery Bypass Grafts, or “CABG” and peripheralbypass grafts) or open up an obstruction (balloon dilation catheters,angioplasty balloons); 2) to relieve pressure (e.g., shunts, drainagetubes and drainage catheters, urinary catheters); 3) to restore orsupport anatomical structures (e.g., endotracheal tubes, tracheostomytubes, and feeding tubes); and 4) for access (e.g., CVC catheters,peritoneal and hemodialysis catheters). Representative examples of tubesinclude catheters, auditory or Eustachian tubes, drainage tubes,tracheotomy tubes (e.g., Durham's tube), endobronchial tubes,endotracheal tubes, esophageal tubes, feeding tubes (e.g., nasogastricor NG tubes), stomach tubes, rectal tubes, colostomy tubes, and a widevariety of grafts (e.g., bypass grafts). See, e.g., PCT Publication No.WO 2015/200718 and U.S. Patent Publ. No. 2017/0196478, as well as PCTPublication No. WO 2016/044651 and U.S. Patent Publ. No. 20160310077,for disclosure of medical tubes and sensors attached thereto. In oneembodiment the medical tube is selected from a catheter, an auditory orEustachian tubes a drainage tube, a tracheotomy tube, an endobronchialtube, an endotracheal tube, an esophageal tube, a feeding tube, astomach tube, a rectal tube, and a colostomy tube.

In one embodiment of the present disclosure, the medical implant is anaesthetic (cosmetic) implant, and the intelligent implant is anaesthetic implant coupled to a sensor. An aesthetic implant refers to anartificial or synthetic prosthesis that has, or can be, implanted into abody. Implants are typically utilized to augment or replace a structurewithin the body, and have been utilized in a wide variety of aestheticapplications, including for example, for facial (e.g., lips, chin,nasal, nasal/labial fold and malar implants), penile, and bodycontouring (e.g., breast, pectoral, calf, buttocks, abdomen andbiceps/triceps) implants. See, e.g., PCT Publication No. WO 2015/200704and U.S. Patent Publ. No. 2017/0181825, as well as PCT Publication No.WO 2016/044651 and U.S. Patent Publ. No. 20160310077, for disclosure ofaesthetic implants and sensors attached thereto. In one embodiment theaesthetic implant is a breast implant.

In one embodiment of the present disclosure, the medical implant is aspinal implant, and the intelligent implant is a spinal implant coupledto a sensor. Examples of spinal devices and implants include pediclescrews, spinal rods, spinal wires, spinal plates, spinal cages,artificial discs, bone cement, as well as combinations of these (e.g.,one or more pedicle screws and spinal rods, one or more pedicle screwsand a spinal plate). In addition medical delivery devices for theplacement of spinal devices and implants, along with one or moresensors, may also be an intelligent medical device according to thepresent disclosure. Examples of medical delivery devices for spinalimplants include kyphoplasty balloons, catheters (including thermalcatheters and bone tunnel catheters), bone cement injection devices,microdiscectomy tools and other surgical tools. See, e.g., PCTPublication No. WO 2015/200720 and U.S. Patent Publ. No. 2017/0196508,as well as PCT Publication No. WO 2016/044651 and U.S. Patent Publ. No.20160310077, for disclosure of spinal implants and sensors attachedthereto, and delivery devices with sensors attached thereto for theplacement of spinal devices, any of which may be an intelligent medicaldevices or an intelligent implant of the present disclosure.

In one embodiment of the present disclosure, the medical device is apiece of orthopedic hardware, which may or may not be implantable, andthe intelligent medical device is a sensor coupled to a piece oforthopedic hardware, which may or may not be implantable orthopedichardware. Examples of orthopedic devices and implants include externalhardware (e.g., casts, braces, external fixation devices, tensors,slings and supports) and internal hardware (e.g., K-wires, pins, screws,plates, and intramedullary devices (e.g., rods and nails)). In additionmedical delivery devices for the placement of orthopedic devices andimplants, along with one or more sensors, may also be an intelligentmedical device of the present disclosure. Examples of medical deliverydevices for orthopedic hardware include drills, drill guides, mallets,guidewires, catheters, bone tunneling catheters, microsurgical tools andgeneral surgical tools. See, e.g., PCT Publication No. WO 2015/200722and U.S. Patent Publ. No. 2017/0196499, as well as PCT Publication No.WO 2016/044651 and U.S. Patent Publ. No. 20160310077, for disclosure oforthopedic hardware and sensors attached thereto, and delivery deviceswith sensors for the placement of orthopedic hardware, all of which maybe intelligent medical devices and intelligent implants of the presentdisclosure.

In one embodiment of the present disclosure, the medical device is amedical polymer that is used in a medical procedure. A wide variety ofpolymers may be used as a medical polymer, where the attached sensor maymonitor the integrity and efficaciousness of the polymer (whetherutilized alone, or as or with another medical device or implant).Medical polymers of the present disclosure can be formed into a vastarray of shapes and sizes which are suitable for medical applications.Representative examples of polymer forms include solid forms such asfilms, sheets, molded, cast, or cut shapes. Other solid forms includeextruded forms which can be made into tubes (e.g., shunts, drainagetubes, and catheters), and fibers which can be knitted into meshes orused to make sutures. Liquid forms of polymers include gels,dispersions, colloidal suspensions and the like. Particularly preferredpolymers for use within the present disclosure are medical polymerswhich are provided in a sterile and/or non-pyrogenic form, and suitablefor use in humans. Representative examples of polymers includepolyester, polyurethanes, silicones, epoxy resin, melamine formaldehyderesin, acetal, polyethyelene terephthalate, polysulphone, polystyrene,polyvinyl chloride, polyamide, polyolefins, polycarbonate, polyethylene,polyamides, polimides, polypropylene, polytetrafluoroethylene, ethylenepropylene diene rubber, styrenes (e.g., styrene butadiene rubber),nitriles (e.g., nitrile rubber), hypalon, polysulphide, butyl rubber,silicone rubber, cellulose, chitosan, fibrinogen, collagen, hyaluronicacid, PEEK, PTFE, PLA, PLGA, PCL and PMMA. See, e.g., PCT PublicationNo. WO 2015/200723 and U.S. Patent Publ. No. 2017/0189553, as well asPCT Publication No. WO 2016/044651 and U.S. Patent Publ. No.20160310077, for disclosure of polymers and sensors attached thereto,all of which may be intelligent medical devices and intelligent implantsof the present disclosure.

In one embodiment of the present disclosure, the medical device is aheart valve, and the intelligent medical device is a heart valve coupledto a sensor. “Heart valve” refers to a device which can be implantedinto the heart of a patient with valvular disease. There are threeprinciple types of heart valves: mechanical, biological, andtissue-engineered (although, for purposes of this disclosuretissue-engineered valves will be considered along with other biologicalvalves). Mechanical valves typically fall into two categories: 1) heartvalves for surgical procedures utilizing a sternotomy or “open heart”procedure (e.g., ‘caged ball’, ‘tilting disc’, bileaflet and trileafletdesigns); and 2) heart valves which are percutaneously implanted (e.g.,either a stent framed (self-expanding stent or balloon-expandable stent)or non-stent framed design) that can often contain valve cusps which arefabricated from biological sources (bovine or porcine pericardium).Tissue-based or ‘biological’ valves are typically made from eitherporcine or bovine sources, and are typically prepared either from thevalve of the animal (e.g., a porcine valve), or from tissue of thepericardial sac (e.g., a bovine pericardial valve or a porcinepericardial valve). Tissue-engineered valves are valves that have beenartificially created on a scaffold (e.g., through the growth of suitablecells on a tissue scaffold). Tissue-engineered valves have not yet beencommercially adopted. See, e.g., PCT Publication No. WO 2015/200707 andU.S. Patent Publ. No. 2017/0196509, as well as PCT Publication No. WO2016/044651 and U.S. Patent Publ. No. 20160310077, for disclosure ofheart valves and sensors attached to heart valves which may be medicaldevices and intelligent medical devices, respectively, of the presentdisclosure. In embodiments, the heart valve is a mechanical heart valve,e.g., a caged ball design, a tilting disc mechanical valve, a bileafletor trileaflet mechanical valve, a self-expanding percutaneous heartvalve, a percutaneous heart valve, or a balloon-expandable percutaneousheart artificial valve. The medical device having a sensor may be aballoon delivery device for a balloon-expandable percutaneous heartvalve.

In one embodiment, the medical implant replaces a joint of a patient,e.g., a knee, shoulder, or hip joint, and allows the patient to have thesame, or nearly the same, mobility as would have been afforded by ahealthy joint. When the medical implant replaces a joint, in oneembodiment the sensor coupled to the implant can monitor displacement ormovement. In general, there are three types of three-dimensional motionthat sensors can detect within and round a joint: core gait (or limbmobility in the case of a shoulder or elbow arthroplasty), macroscopicinstability, and microscopic instability. While these motions will bediscussed associated with a TKA implant, it is understood they may alsoapply to total hip, shoulder, elbow, and ankle arthroplasty. See, e.g.,PCT Publication Nos. WO 2014/144107, WO 2014/209916, WO 2016/044651, andWO 2017/165717 for disclosure of medical implants that may replace thejoint of a patient, and intelligent versions thereof, for use in thepresent disclosure.

In one embodiment, the medical implant is a knee implant, and inparticular a total knee implant for total knee arthroscopy. Sensorsattached to the total knee implant of the present disclosure can monitorand characterize movement of the knee implant, where that movement maytake the form of, e.g., core gait, macroscopic instability andmicroscopic instability as discussed below.

FIG. 1 is a perspective view of an exemplary embodiment of a reportingprocessor 10 that can be utilized to implement the exemplary intelligentimplant depicted in the exemplary embodiment shown in FIG. 2. In theembodiment shown in FIG. 1, the implantable reporting processor 10includes an outer casing 12 that encloses a power component (battery)14, an electronics assembly 16, and an antenna 20. One component of thecasing is the radome 18, used to cover and protect the antenna whichallows the implantable reporting processor to receive and transmitinformation. The outer casing 12 can include a set-screw engagement hole22, which can be utilized to physically attach the reporting processor10 to a tibial plate 32, as depicted in FIG. 2.

FIG. 2 is a perspective view of a tibial component 30 that can beutilized to implement one exemplary embodiment of the presentdisclosure. For example, the tibial component 30 shown in FIG. 2 caninclude an implanted medical device for a TKA, such as a tibialextension and the like. Referring to the exemplary embodiment shown inFIG. 2, the tibial component 30 includes a tibial plate 32 physicallyattached to an upper surface of a tibia 34. For example, the tibialplate 32 can be a base plate section of an artificial knee joint(prosthesis) that can be implanted during a surgical procedure, such asa TKA. Prior to, or during the surgical procedure, the implantablereporting processor 10 from FIG. 1 can be physically attached to thetibial plate 32 and also implanted into the tibia 34. For the exemplaryembodiment shown in FIG. 2, the tibial component 30 includes the tibialplate 32 and the reporting processor 10, which are surgically implantedto form a tibial extension 36.

Core gait is described as the motion associated with basic locomotion.It occurs predominantly in the sagittal plane and has a frequency in therange of 0.5 Hz to 5 Hz. Most commonly, this can be thought of as thebasic walking motion beginning with toe off, the leg swinging forwardbending at the knee in combination with hip motion, the leg extendingending with a heel strike, and rolling on the foot back to a toe offposition.

Macroscopic instability is a subset of motion within the core gait andis associated with musculoskeletal instability when loading the jointduring the gait process. As an example, simplistically, this can bethought of as uncontrolled medial lateral and/or anterior posteriormotion when getting out of a chair or walking up or down stairs and hasa frequency in the range of 2 Hz to 20 Hz and would cover a range ofmotion from 5 mm to 10 cm.

Microscopic instability is a further subset of motion associated withmotion within the TKA joint due to misalignment between the femoralcomponent and the tibial plate and its tibial insert. This motion canoccur in the anterior posterior plane and/or the medical lateral planeand has a frequency in the range of 5 Hz to 50 Hz covering a range ofmotion from 0.1 mm to 2 cm in any or a combination of these directions.This motion can be due to improper sizing of the implant at the initialprocedure, changes in musculoskeletal structure associated with weightloss and/or further injury, and/or wear in the joint causing changes inthe polymer puck geometry and associated fit with the femoral component.In addition, this motion may be caused by loosening of the tibialcomponent itself due to bone subsidence and/or poor bone structureassociated with osteoporosis or other metabolic disorders effecting bonedensity. It is also understood, that the noted microscopic instabilitymay be due to a combination of the afore-mentioned motion modalities.Both macroscopic and microscopic instability can be associated with painand decrease in quality of life metrics for a patient and may needintervention to resolve.

A sensor modality implanted in the bone and integrated into the TKA, oreven a sensor implanted just in the bone and not necessarily coupled tothe implant, resolves the signal fidelity and compliance limits ofexternal devices. However, there is still an unmet need to identify thesensor data signatures indicative of instability, with sufficientfidelity to enable “early warning system” before onset of bone erosion,TKA hardware degradation, and pain that will require moreinvasive/expensive interventions. The present disclosure addresses thisneed.

In embodiments, the present disclosure provides method and devices thatinclude: an implanted sensor coupled to TKA hardware and/or coupled tobone, where the sensor has sufficient sensitivity and specificity todetect motion of the tibia (or the tibial component of the TKI)consistent with identifying an instability signature. “Instabilitysignature” is defined as having a characteristic frequency response ofgreater than about 20 Hz, or greater than about 25 Hz, or greater thanabout 30 Hz, and less than about 90 Hz, or less than about 100 Hz, orless than about 110 Hz, and indicates that the TKA hardware is notfixedly engaged with the tibia bone. Normal kinematic motions duringnormal human locomotion are typically less than about 20 Hz, whilemovement of the device associated with wear, abrasion, and lack ofosteointegration (referred to collectively as degradation) is typicallyassociated with movement of greater than 100 Hz. Device instabilitytypically provides motion between about 20 Hz and about 100 Hz. Thepresent disclosure provides a sensor coupled to an intelligent implantof the present disclosure which has sufficient dynamic range that it candetect and distinguish between normal kinematic motion (typically lessthan about 20 Hz), instability of the implant (typically about 20-100Hz), and degradation, or lack of osteo-integration of the implant(typically greater than about 100 Hz). The sensor may have sufficientdynamic range that is high enough to not be saturated by normalkinematic motion, but sensitive enough detect small motions/impactsindicative of “instability signature”. In addition, the sensor may havesufficient frequency response and sampling rate to differentiate withoutaliasing; i) normal kinematic motion, ii) instability signatures, andiii) degradation signatures.

In embodiments, the present disclosure provides medical devices coupledto a performing motion sensor (e.g., one or more of a sensor selectedfrom accelerometer that detects acceleration, and a gyroscope), and alsoprovides algorithms that can quantify the extent of instability; i.e., 1mm movement vs. 2 mm movement or 5 degrees of movement vs. 10 degrees ofmovement, where the extend of instability is determined from a definedtransient signature meeting the temporal and spectral definition. Fromthis information, the extent of instability can be assessed over timeand a “threshold for intervention” may be determined based on; i)clinical data, ii) anatomical thresholds, iii) TKA device design limitsand analysis, as well as other factors.

In one aspect the present disclosure provides a reporting processor thatis intended to be implanted with a medical device, e.g., a prosthesis,where the reporting processor monitors the state of the device afterimplantation, typically by obtaining kinematic data in the range ofabout 10-120 Hz. This reporting processor is also referred to as animplantable reporting processor or IRP. As discussed herein, the stateof the device may include the integrity of the device, the movement ofthe device, the forces exerted on the device and other informationrelevant to the implanted device. The present disclosure also providesmedical devices having a structure such that they can be readily fittedwith an IRP. An implantable medical device that has been fitted with anIRP is referred to herein as an intelligent implant, in recognition thatthe implant is monitoring its own state or condition to thereby obtaindata, where that data is stored in the implant and then as needed, thatdata is transmitted to a separate device for review by, e.g., aphysician.

For example, an intelligent implantable device of the present disclosurehaving suitable internal electronic components can be utilized tomonitor and measure the movements of a surgical patient's syntheticjoint (prosthesis) implanted via a total knee arthroplasty (TKA), storethe measurement data and unique identification information of theprosthetic components, and transfer the data to an external recipient(e.g., doctor, clinician, medical assistant, etc.) as required. The IRPwill include one or more sensors, such as gyroscopes, accelerometers,and temperature and pressure sensors, and these sensors may be locatedanywhere within the IRP outer casing, e.g., they may all be located onthe PC board. In one embodiment, e.g., when the intelligent implant is ajoint prosthesis, the IRP makes kinematic measurements, and in anotherembodiment the IRP makes only kinematic measurements. Thus, anintelligent joint implant may include sensors for kinematicmeasurements, to determine the movements experienced by the implantedprosthesis.

The intelligent medical devices of the present disclosure may include acomponent for a total or partial joint replacement, such as occursduring a total knee arthroplasty (TKA) where the IRP may be a componentof, or attached to, a tibial component, a femoral component and/or atibial extension; or such as occurs during a hip replacement, where theIRP may be a component of, or attached to, the femoral component or theacetabular component for hip replacements; or such as occurs during ashoulder replacement, where the IRP may be a component of, or attachedto, the humeral component for shoulder replacements. Other examples of amedical device that may be combined with an IRP to provide anintelligent implant include a breast implant, a lumbar interbody cage, aspinal artificial disc, a dynamic hip screw, and a leg intramedullaryrod.

The IRP and the medical device are each intended to be implanted into aliving subject, e.g., a mammal, e.g., a human, horse, dog, etc.Accordingly, in one embodiment the IRP is sterile, e.g., is treated withsterilizing radiation or is treated with ethylene oxide. In anotherembodiment, the intelligent implant comprising the IRP and the medicaldevice is sterile, again optionally by treatment with sterilizingradiation or ethylene oxide, as two examples. In order to be protectedfrom the in vivo environment, in one embodiment the IRP is hermeticallysealed, so that fluids cannot enter into the IRP. The subject withinwhom the medical device has been implanted may alternatively be referredto herein as the patient. In one embodiment, the subject/patient is ahuman.

The implantable device needs to be sturdy as well as small orspace-efficient because of the limited space within the body and/orwithin the prosthetic implant to place such devices. Challenges to thecommercial success of an implantable device with internal electroniccomponents and either internal or external transmitting antennae arethat the devices and/or the transmitting antennae should not beunsuitably large, their power consumption should allow them to operatefor a suitably long period of time, i.e., not for limited durations, andthey should not be adversely affected by their local biologicenvironment. An IRP of the present disclosure may have suitable internalor external space-efficient and/or power-efficient antennae.

The intelligent implant will optionally have a power source needed torun the electronics inside the IRP that measures, records and transmitsdata concerning the state of the implant. Some medical implants alreadyhave a power supply. An example of an in-vivo implantable prosthesisthat can improve the function of an organ and which has a power supplyis an implantable atrial defibrillator, which detects when a heartenters into an abnormal rhythm commonly known as “atrial fibrillation,”and which generates one or more electrical pulses to restore the heartto a normal sinus rhythm. Typically, this power supply is in the form ofa battery.

Because the electrical charge on the battery may last a relatively shortperiod of time, the prosthesis is typically located in a region of thebody from which it is practical to remove the prosthesis to replace thebattery, or to recharge the battery. For example, an atrialdefibrillator is typically implanted just under the skin of a patient'schest. To replace the battery, a surgeon makes an incision, removes theold defibrillator, implants a new defibrillator containing a newbattery, and closes the incision. Or, the patient or a physician, suchas a cardiologist, recharges the battery, without removing thedefibrillator from the subject, by placing, over the implanteddefibrillator, a device that recharges the battery via inductive(sometimes called magnetic) coupling.

Unfortunately, removing a prosthesis to replace a battery is oftenundesirable, at least because it involves an invasive procedure that canbe relatively expensive and that can have adverse side effects, such asinfection and soreness. Although inductively recharging an implantedbattery is non-invasive, it may be impractical or impossible to locatethe prosthesis such that the battery may be inductively recharged.Additionally, the size of the coils necessary to transfer power arelarge relative to the device, and this can pose a problem in the limitedspace available within the body. The time for re-charging can beexcessive, lack of coil alignment can cause excess heat generation,which potentially can damage surrounding tissue, and the inductivebattery configuration can render the implant incompatible with MRI use.Additionally, battery chemistries that are compatible with recharging(i.e., secondary cell) generally have a significantly reducedenergy-storage capacity in comparison to batteries of similar sizeconstructed using non-rechargeable chemistries (i.e., primary cell).

An alternative that can overcome this latter problem is to implant thebattery remotely from the implanted prosthesis in a location in which itis practical to inductively recharge the battery. An advantage ofimplanting the battery remotely from the implanted prosthesis is thatthe battery can be made larger, and thus longer lasting, than it wouldbe if it were located inside of the prosthesis. But implanting thebattery remotely from the implanted prosthesis can have severaldisadvantages. For example, even though the battery is suitably locatedfor inductive recharging, the recharging equipment can be too expensiveor too complex for home use, the patient may forget to recharge thedevice, and periodically visiting the doctor to recharge the battery maybe inconvenient and expensive for the patient. Furthermore, it can bedifficult to implant the wires used to couple the battery to the remote(from the battery) implanted prosthesis or if powering the implantsensors wirelessly from the rechargeable battery, the sensors may belimited in measurement capability. Moreover, because the battery istypically implanted just below the skin to heighten theinductive-coupling coefficient, it can be visible, and thusembarrassing, to the patient, and it can make the patient physicallyuncomfortable.

Thus, the implantable reporting process (IRP) may contain a power source(e.g., a battery) as well as mechanisms to manage the power output of animplanted power source, so that the power source will provide power fora sufficient period of time regardless of the location of the powersource within a body of a patient. The IRP may contain the only powersource present in the intelligent implant.

An example of a battery suitable for use with an implantable reporterprocessor includes a container sized to fit inside of bone of a livingpatient, and has a lifetime, such as years, that is sufficient to powerthe electronic circuitry within the implantable reporter processor for aperiod of time that is suitable for a prosthesis in which theimplantable reporter processor is installed. The battery can beconfigured for disposal directly in the bone, or can be configured fordisposal in a portion of the implantable reporting processor that isdisposed in the bone. Or, the battery can be configured for disposal ina region of a living body other than a bone where it is impractical torecharge the battery, and where it is impractical to replace the batterybefore replacing a prosthesis or other device with which the battery isassociated.

The IRP will typically comprise an outer casing that encloses aplurality of components. Exemplary suitable IRP components include asignal portal, an electronics assembly, and a power source. In oneembodiment, the IRP does include each of a signal portal, an electronicsassembly and a power source. The signal portal functions to receive andtransmit wireless signals, and may contain, for example, an antenna fortransmitting the wireless signals. The electronics assembly includes acircuit assembly which may comprise, e.g., a PC board and electricalcomponents formed on one or more integrated circuits (ICs) or chips,such as a radio transmitter chip, a real-time clock chip, one or moresensor components, e.g., an Inertial Measurement Unit (IMU) chip,temperature sensor, pressure sensor, pedometer, a memory chip, and thelike. In addition, the electronics assembly may include a headerassembly which provides a communication interface between the circuitassembly and the signal portal (e.g., antenna). The power sourceprovides the energy needed to operate the IRP, and may be, for example,a battery. The IRP will also include one or more sensors, such asgyroscopes, accelerometers, pedometers, and temperature and pressuresensors, and these sensors may be located anywhere within the IRP outercasing, e.g., they may all be located on the PC board. More precisely,an embodiment of the present disclosure is directed to space-efficient,printed-circuit assemblies (PCAs) for an implantable reporting processor(IRP). The implantable reporting processor may also include a pluralityof transmitting antennae structured in different configurations. Assuch, an embodiment of the present disclosure is directed to a pluralityof enhanced space-efficient and power-efficient antenna configurationsfor an implantable reporting processor, such as an IRP.

An example of an implantable reporting processor includes an outercasing, or housing, sized to fit in, or to form a part of, a prosthesisthat has at least a portion designed to fit in a bone of a livingpatient. Electronic circuitry is disposed in the housing and isconfigured to provide, to a destination outside of a patient's body,information related to the prosthesis. The battery is also disposed inthe housing and is coupled to the electronic circuitry.

An example of a prosthesis includes a receptacle for receiving theimplantable reporting processor, which can be designed to fit into acavity formed in a bone of a living patient. For example, theimplantable reporting processor can be disposed in, or form part of, atibial component or tibial extension of a knee prosthesis, where thetibial component or tibial extension is designed to fit into a cavityformed in the tibia of the living patient.

The power profile of the electronic circuitry of the implantablereporting processor can be configured so that the battery has a desiredanticipated lifetime suitable for the type of prosthesis (or otherdevice) with which the battery is associated. For example, such adesired anticipated lifetime may range from 1 to 15+ years, e.g., 10years. An embodiment of such circuitry includes a supply node configuredto be coupled to a battery, at least one peripheral circuit, aprocessing circuit coupled to the supply node and configured to couplethe at least one peripheral circuit to the supply node, and a timingcircuit coupled to the supply node and configured to activate theprocessing circuit at a set time or set times.

A base station may be provided to facilitate communications with theimplantable reporting processor, and to act as an interface between thereporting processor and another computing system, such as a database orremote server on “the cloud,” before and after the implantable reportingprocessor is implanted in the body of a patient as part of a prosthesis.The base station can have different configurations. For example, thebase station can be configured for use by a surgeon or otherprofessional before the prosthesis is implanted. The base station alsocan be configured for use in the residence of the patient. For example,the base station can be configured to poll the implantable reportingprocessor, periodically and automatically (for example, while thepatient is sleeping), for information that the processor obtains orgenerates regarding the prosthesis, and to provide this information tothe other computing system for storage or analysis via a wirelessinternet connection. And the base station can be configured for use in adoctor's office while the doctor is checking the operation and functionof the prosthesis and the patient's health as it relates to theprosthesis. Furthermore, the network to which the base station belongscan include a voice-command device (e.g., Amazon Echo®, Amazon Dot®,Google Home®) that is configured to interact with the base station.

See, e.g., U.S. Publication No. 2016/0310077, titled Devices, Systemsand Methods for Using and Monitoring Medical Devices, which isincorporated herein in its entirety, for disclosure of medical deviceswith sensors that may be used as an intelligent implant according to thepresent disclosure, optionally supplemented as described herein. Seealso, e.g., PCT Publication No. WO 2017/165717, titled ImplantableReporting Processor for an Intelligent Implant, which is incorporatedherein in its entirety, for disclosure of medical devices with sensorsthat may be used as an intelligent implant according to the presentdisclosure, optionally supplemented as described herein.

B. Systems with Intelligent Implants

An intelligent implant may be a component of a system of the presentdisclosure that includes one or more of 1) a sensor that detects and/ormeasures the functioning of the implant and/or the immediate environmentaround the implant and/or the activity of the patient, 2) memory thatstores data from that detection and/or measuring, 3) an antenna thattransmits that data; 4) a base station that receives the data generatedby the sensor and may transmit the data and/or analyzed data to acloud-based location; 5) a cloud-based location where data may be storedand analyzed, and analyzed data may be stored and/or further analyzed;6) a receiving terminal that receives output from the cloud-basedlocation, where that receiving terminal may be accessed, e.g., by ahealth care professional or an insurance company or the manufacturer ofthe implant, and the output may identify the status of the implantand/or the functioning of the implant and/or the status of the patientwho has received the implant, and may also provide recommendations foraddressing any concerns raised by analysis of the original data. Systemsof the present disclosure may be illustrated using a kinematicimplantable device as the intelligent implant, as provided in thefollowing paragraphs. However, these systems may be employed for anyintelligent medical device, including the intelligent medical devicesidentified herein.

See, e.g., U.S. Publication No. 2016/0310077, titled Devices, Systemsand Methods for Using and Monitoring Medical Devices, which isincorporated herein in its entirety, for disclosure of systems accordingto the present disclosure, optionally supplemented as described herein.See also, e.g., PCT Publication No. WO 2017/165717, titled ImplantableReporting Processor for an Intelligent Implant, which is incorporatedherein in its entirety, for disclosure of systems according to thepresent disclosure, optionally supplemented as described herein.

C. Joint Implant and Systems with Joint Implant

FIG. 3 illustrates a context diagram of a kinematic implantable deviceenvironment 1000. In the environment, a kinematic implantable device1002 is implanted by a medical practitioner (not shown in FIG. 3) in thebody of a patient (not shown in FIG. 3). The kinematic implantabledevice 1002 is arranged to collect data including operational data ofthe device 1002 along with kinematic data associated with particularmovement of the patient or particular movement of a portion of thepatient's body, for example, one of the patient's knees. The kinematicimplantable device 1002 communicates with one or more base stations orone or more smart devices during different stages of monitoring thepatient.

For example, in association with a medical procedure, a kinematicimplantable device 1002 is implanted in the patient's body. Coetaneouswith the medical procedure, the kinematic implantable device 1002communicates with an operating room base station (not shown in FIG. 3).Subsequently, after sufficient recovery from the medical procedure, thepatient returns home wherein the kinematic implantable device 1002 isarranged to communicate with a home base station 1004. At other times,the kinematic implantable device 1002 is arranged to communicate with adoctor office base station (not shown in FIG. 3). The kinematicimplantable device 1002 communicates with each base station via a shortrange network protocol, such as the medical implant communicationservice (MICS), the medical device radio communications service(MedRadio), or some other wireless communication protocol suitable foruse with the kinematic implantable device 1002.

The kinematic implantable device 1002 is implanted into a body of apatient (not shown in FIG. 3). The kinematic implantable device 1002 maybe a standalone medical device or it may be a component in a largermedical device, such as an artificial joint (e.g., a knee replacement, ahip replacement, a vertebral device, or the like), a breast implant, afemoral rod, or some other implanted medical device that can desirablycollect and provide in situ kinematic data, operational data, or otheruseful data.

The kinematic implantable device 1002 includes one or more sensors tocollect information and kinematic data associated with the use of thebody part to which the kinematic implantable device 1002 is associated.For example, the kinematic implantable device 1002 may include aninertial measurement unit that includes gyroscope(s), accelerometer(s),pedometer(s), or other kinematic sensors to collect acceleration datafor the medial/lateral, anterior/posterior, and anterior/inferior axesof the associated body part; angular velocity for the sagittal, frontal,and transvers planes of the associated body part; force, stress,tension, pressure, duress, migration, vibration, flexure, rigidity, orsome other measurable data.

The kinematic implantable device 1002 collects data at various differenttimes and at various different rates during a monitoring process of thepatient. In some embodiments, the kinematic implantable device 1002 mayoperate in a plurality of different phases over the course of monitoringthe patient so that more data is collected soon after the kinematicimplantable device 1002 is implanted into the patient, but less data iscollected as the patient heals and thereafter.

In one non-limiting example, the monitoring process of the kinematicimplantable device 1002 may include three different phases. A firstphase may last for four months where kinematic data is collected once aday for one minute, every day of the week. After the first phase, thekinematic implantable device 1002 transitions to a second phase thatlasts for eight months and collects kinematic data once a day for oneminute, two days a week. And after the second phase, the kinematicimplantable device 1002 transitions to a third phase that last for nineyears and collects kinematic data one day a week for one minute for thenext nine years. Of course, the time periods associated with each phasemay be longer, shorter, and otherwise controllable; for example, thetime periods can be selected to be compatible with time periodsspecified by medical-insurance telemedicine codes so that a physicianbilling under telemedicine codes can collect the maximum reimbursementallowed by a medical insurer. The type and amount of data collected mayalso be controllable. The added benefit of this passive monitoringprocess is that after the first phase of monitoring, the patient will beunaware of when data is being collected. Thus, the collected data willbe protected from potential bias.

Along with the various different phases, the kinematic implantabledevice 1002 can operate in various modes to detect different types ofmovements. In this way, when a predetermined type of movement isdetected, the kinematic implantable device 1002 can increase, decrease,or otherwise control the amount and type of kinematic data and otherdata that is collected.

In one example, the kinematic implantable device 1002 may use apedometer to determine if the patient is walking. If the kinematicimplantable device 1002 measures that a determined number of stepscrosses a threshold value within a predetermined time, then thekinematic implantable device 1002 may determine that the patient iswalking. In response to the determination, the amount and type of datacollected can be started, stopped, increased, decreased, or otherwisesuitably controlled. The kinematic implantable device 1002 may furthercontrol the data collection based on certain conditions, such as whenthe patient stops walking, when a selected maximum amount of data iscollected for that collection session, when the kinematic implantabledevice 1002 times out, or based on other conditions. After data iscollected in a particular session, the kinematic implantable device 1002may stop collecting data until the next day, the next time the patientis walking, after previously collected data is offloaded (e.g., bytransmitting the collected data to the home base station 1004), or inaccordance with one or more other conditions.

The amount and type of data collected by a kinematic implantable device1002 may be different from patient to patient, and the amount and typeof data collected may change for a single patient. For example, amedical practitioner studying data collected by the kinematicimplantable device 1002 of a particular patient may adjust or otherwisecontrol how the kinematic implantable device 1002 collects future data.

The amount and type of data collected by a kinematic implantable device1002 may be different for different body parts, for different types ofmovement, for different patient demographics, or for other differences.Alternatively, or in addition, the amount and type of data collected maychange overtime based on other factors, such as how the patient ishealing or feeling, how long the monitoring process is projected tolast, how much battery power remains and should be conserved, the typeof movement being monitored, the body part being monitored, and thelike. In some cases, the collected data is supplemented with personallydescriptive information provided by the patient such as subjective paindata, quality of life metric data, co-morbidities, perceptions orexpectations that the patient associates with the kinematic implantabledevice 1002, or the like.

In some embodiments, the kinematic implantable device 1002 is implantedinto a patient to monitor movement or other aspects of a particular bodypart. Implantation of the kinematic implantable device 1002 into thepatient may occur in an operating room. As used herein, operating roomincludes any office, room, building, or facility where the kinematicimplantable device 1002 is implanted into the patient. For example, theoperating room may be a typical operating room in a hospital, anoperating room in a surgical clinic or a doctor's office, or any otheroperating theater where the kinematic implantable device 1002 isimplanted into the patient.

The operating room base station (not shown in FIG. 3) is utilized toconfigure and initialize the kinematic implantable device 1002 inassociation with the kinematic implantable device 1002 being implantedinto the patient. A communicative relationship is formed between thekinematic implantable device 1002 and the operating room base station,for example, based on a polling signal transmitted by the operating roombase station and a response signal transmitted by the kinematicimplantable device 1002.

Upon forming a communicative relationship, which will often occur priorto implantation of the kinematic implantable device 1002, the operatingroom base station (not shown in FIG. 3) transmits initial configurationinformation to the kinematic implantable device 1002. This initialconfiguration information may include, but is not limited to, a timestamp, a day stamp, an identification of the type and placement of thekinematic implantable device 1002, information on other implantsassociated with the kinematic implantable device, surgeon information,patient identification, operating room information, and the like.

In some embodiments, the initial configuration information is passedunidirectionally; in other embodiments, initial configuration is passedbidirectionally. The initial configuration information may define atleast one parameter associated with the collection of kinematic data bythe kinematic implantable device 1002. For example, the configurationinformation may identify settings for one or more sensors on thekinematic implantable device 1002 (e.g., accelerometer range,accelerometer output data rate, gyroscope range, gyroscope output datarate, and the like) for each of one or more modes of operation). Theconfiguration information may also include other control information,such as an initial mode of operation of the kinematic implantable device1002, a particular movement that triggers a change in the mode ofoperation, radio settings, data collection information (e.g., how oftenthe kinematic implantable device 1002 wakes up to collected data, howlong it collects data, how much data to collect), home base station1004, smart device 1005, and connected personal assistant 1007identification information, and other control information associatedwith the implantation or operation of the kinematic implantable device1002. Examples of the connected personal assistant 1007, which also canbe called a smart speaker, include Amazon Echo®, Amazon Dot®, GoogleHome®, Philips® patient monitor, Comcast's health-tracking speaker, andApple HomePod®.

In some embodiments, the configuration information may be pre-stored onthe operating room base station (not shown in FIG. 3) or an associatedcomputing device. In other embodiments, a surgeon, surgical technician,or some other medical practitioner may input the control information andother parameters to the operating room base station for transmission tothe kinematic implantable device 1002. In at least one such embodiment,the operating room base station may communicate with an operating roomconfiguration computing device (not shown in FIG. 3). The operating roomconfiguration computing device includes an application with a graphicaluser interface that enables the medical practitioner to inputconfiguration information for the kinematic implantable device 1002. Invarious embodiments, the application executing on the operating roomconfiguration computing device may have some of the configurationinformation predefined, which may or may not be adjustable by themedical practitioner.

The operating room configuration computing device (not shown in FIG.100) communicates the configuration information to the operating roombase station (not shown in FIG. 3) via a wired or wireless networkconnection (e.g., via a USB connection, Bluetooth connection, BluetoothLow Energy (BTLE) connection, or Wi-Fi connection), which in turncommunicates it to the kinematic implantable device 1002.

The operating room configuration computing device (not shown in FIG. 3)may also display information regarding the kinematic implantable device1002 or the operating room base station (not shown in FIG. 3) to thesurgeon, surgical technician, or other medical practitioner. Forexample, the operating room configuration computing device may displayerror information if the kinematic implantable device 1002 is unable tostore or access the configuration information, if the kinematicimplantable device 1002 is unresponsive, if the kinematic implantabledevice 1002 identifies an issue with one of the sensors or radio duringan initial self-test, if the operating room base station (not shown inFIG. 3) is unresponsive or malfunctions, or for other reasons.

Although the operating room base station (not shown in FIG. 3) and theoperating room configuration computing device (not shown in FIG. 3) areillustrated as separate devices, embodiments are not so limited; rather,the functionality of the operating room configuration computing deviceand the operating room base station may be included in a singlecomputing device or in separate devices as illustrated. In this way, themedical practitioner may be enabled in one embodiment to input theconfiguration information directly into the operating room base station.

Once the kinematic implantable device 1002 is implanted into the patientand the patient returns home, the home base station 1004, the smartdevice 1005 (e.g., the patient's smart phone), the connected personalassistant 1007, or two or more of the home base station, and the smartdevice, and the connected personal assistant can communicate with thekinematic implantable device 1002. The kinematic implantable device 1002can collect kinematic data at determined rates and times, variable ratesand times, or otherwise controllable rates and times. Data collectioncan start when the kinematic implantable device 1002 is initialized inthe operating room, when directed by a medical practitioner, or at somelater point in time. At least some data collected by the kinematicimplantable device 1002 may be transmitted to the home base station 1004directly, to the smart device 1005 directly, to the connected personalassistant 1007 directly, to the base station via one or both of thesmart device and the connected personal assistant, to the smart devicevia one or both of the base station and the connected personalassistant, or to the connected personal assistant via one or both of thesmart device and the base station. Here, “one or both” means via an itemalone, and via both items serially or in parallel. For example, datacollected by the kinematic implantable device 1002 may be transmitted tothe home base station 1004 via the smart device 1005 alone, via theconnected personal assistant 1007 alone, serially via the smart deviceand the connected personal assistant, serially via the connectedpersonal assistant and the smart device, and directly, and possiblycontemporaneously, via both the smart device and the connected personalassistant. Similarly, data collected by the kinematic implantable device1002 may be transmitted to the smart device 1005 via the home basestation 1004 alone, via the connected personal assistant 1007 alone,serially via the home base station and the connected personal assistant,serially via the connected personal assistant and the home base station,and directly, and possibly contemporaneously, via both the home basestation and the connected personal assistant. Further in example, datacollected by the kinematic implantable device 1002 may be transmitted tothe connected personal assistant 1007 via the smart device 1005 alone,via the home base station 1004 alone, serially via the smart device andthe home base station, serially via the home base station and the smartdevice, and directly, and possibly contemporaneously, via both the smartdevice and the home base station.

In various embodiments, one or more of the home base station 1004, thesmart device 1005, and the connected personal assistant 1007 pings thekinematic implantable device 1002 at periodic, predetermined, or othertimes to determine if the kinematic implantable device 1002 is withincommunication range of one or more of the home base station, the smartdevice, and the connected personal assistant. Based on a response fromthe kinematic implantable device 1002, one or more of the home basestation 1004, the smart device 1005, and the connected personalassistant 1007 determines that the kinematic implantable device 1002 iswithin communication range, and the kinematic implantable device 1002can be requested, commanded, or otherwise directed to transmit the datait has collected to one or more of the home base station 1004, the smartdevice 1005, and the connected personal assistant 1007.

Each of one or more of the home base station 1004, the smart device1005, and the connected personal assistant 1007 may, in some cases, bearranged with a respective optional user interface. The user interfacemay be formed as a multimedia interface that unidirectionally orbidirectionally passes one or more types of multimedia information(e.g., video, audio, tactile, etc.). Via the respective user interfaceof one or more of the home base station 1004, the smart device 1005, andthe connected personal assistant 1007, the patient (not shown in FIG. 3)or an associate (not shown in FIG. 3) of the patient may enter otherdata to supplement the kinematic data collected by the kinematicimplantable device 1002. A user, for example, may enter personallydescriptive information (e.g., age change, weight change), changes inmedical condition, co-morbidities, pain levels, quality of life, anindication of how the implanted prosthesis 1002 “feels,” or othersubjective metric data, personal messages for a medical practitioner,and the like. In these embodiments, the personally descriptiveinformation may be entered with a keyboard, mouse, touch-screen,microphone, wired or wireless computing interface, or some other inputmeans. In cases where the personally descriptive information iscollected, the personally descriptive information may include, orotherwise be associated with, one or more identifiers that associate theinformation with unique identifier of the kinematic implantable device1002, the patient, an associated medical practitioner, an associatedmedical facility, or the like.

In some of these cases, a respective optional user interface of each ofone or more of the home base station 1004, the smart device 1005, andthe connected personal device 1007 may also be arranged to deliverinformation associated with the kinematic implantable device 1002 to theuser from, for example, a medical practitioner. In these cases, theinformation delivered to the user may be delivered via a video screen,an audio output device, a tactile transducer, a wired or wirelesscomputing interface, or some other like means.

In embodiments where one or more of the home base station 1004, thesmart device 1005, and the connected personal assistant 1007 arearranged with a user interface, which may be formed with an internaluser interface arranged for communicative coupling to a patient portaldevice. The patent portal device may be smartphone, a tablet, abody-worn device, a weight or other health measurement device (e.g.,thermometer, bathroom scale, etc.), or some other computing devicecapable of wired or wireless communication. In these cases, the user isable to enter the personally descriptive information, and the user alsomay be able to receive information associated with the implantabledevice 1002.

The home base station 1004 utilizes a home network 1006 of the patientto transmit the collected data (i.e., kinematic data and in some cases,personally descriptive information) to cloud 1008. The home network1006, which may be a local area network, provides access from the homeof the patient to a wide area network, such as the internet. In someembodiments, the home base station 1004 may utilize a Wi-Fi connectionto connect to the home network 1006 and access the internet. In otherembodiments, the home base station 1004 may be connected to a homecomputer (not shown in FIG. 3) of the patient, such as via a USBconnection, which itself is connected to the home network 1006.

The smart device 1005 can communicate with the kinematic implantabledevice 1002 directly via, for example, Blue Tooth® compatible signals,and can utilize the home network 1006 of the patient to transmit thecollected data (i.e., kinematic data and in some cases, personallydescriptive information) to cloud 1008, or can communicate directly withthe cloud, for example, via a cellular network. Alternatively, the smartdevice 1005 is configured to communicate directly with one or both ofthe base station 1004 and the connected personal assistant 1007 via, forexample, Blue Tooth® compatible signals, and is not configured tocommunicate directly with the kinematic implantable device 1002.

Furthermore, the connected personal assistant 1007 can communicate withthe kinematic implantable device 1002 directly via, for example, BlueTooth® compatible signals, and can utilize the home network 1006 of thepatient to transmit the collected data (i.e., kinematic data and in somecases, personally descriptive information) to cloud 1008, or cancommunicate directly with the cloud, for example, via a modem/internetconnection or a cellular network. Alternatively, the connected personalassistant 1007 is configured to communicate directly with one or both ofthe base station 1004 and the smart device 1005 via, for example, BlueTooth® compatible signals, and is not configured to communicate directlywith the kinematic implantable device 1002.

Along with transmitting collected data to the cloud 1008, one or more ofthe home base station 1004, the smart device 1005, and the connectedpersonal assistant 1007 may also obtain data, commands, or otherinformation from the cloud 1008 directly or via the home network 1006.One or more of the home base station 1004, the smart device 1005, andthe connected personal assistant 1007 may provide some or all of thereceived data, commands, or other information to the kinematicimplantable device 1002. Examples of such information include, but arenot limited to, updated configuration information, diagnostic requeststo determine if the kinematic implantable device 1002 is functioningproperly, data collection requests, and other information.

The cloud 1008 may include one or more server computers or databases toaggregate data collected from the kinematic implantable device 1002, andin some cases personally descriptive information collected from apatient (not shown in FIG. 3), with data collected from other kinematicimplantable devices (not illustrated), and in some cases personallydescriptive information collected from other patients. In this way, thecloud 1008 can create a variety of different metrics regarding collecteddata from each of a plurality of kinematic implantable devices that areimplanted into separate patients. This information can be helpful indetermining if the kinematic implantable devices are functioningproperly. The collected information may also be helpful for otherpurposes, such as determining which specific devices may not befunctioning properly, determining if a procedure or condition associatedwith the kinematic implantable device is helping the patient (e.g., ifthe knee replacement is operating properly and reducing the patient'spain), and determining other medical information.

At various times throughout the monitoring process, the patient may berequested to visit a medical practitioner for follow up appointments.This medical practitioner may be the surgeon who implanted the kinematicimplantable device 1002 in the patient or a different medicalpractitioner that supervises the monitoring process, physical therapy,and recovery of the patient. For a variety of different reasons, themedical practitioner may want to collect real-time data from thekinematic implantable device 1002 in a controlled environment. In somecases, the request to visit the medical practitioner may be deliveredthrough a respective optional bidirectional user interface of each ofone or more of the home base station 1004, the smart device 1005, andthe connected personal assistant 1007.

A medical practitioner utilizes the doctor office base station (notshown in FIG. 3), which communicates with the kinematic implantabledevice 1002, to pass additional data between the doctor office basestation and the kinematic implantable device 1002. Alternatively, or inaddition, the medical practitioner utilizes the doctor office basestation (not shown in FIG. 3) to pass commands to the kinematicimplantable device 1002. In some embodiments, the doctor office basestation instructs the kinematic implantable device 1002 to enter ahigh-resolution mode to temporarily increase the rate or type of datathat is collected for a short time. The high-resolution mode directs thekinematic implantable device 1002 to collect different (e.g., large)amounts of data during an activity where the medical practitioner isalso monitoring the patient.

In some embodiments, the doctor office base station (not shown in FIG.3) enables the medical practitioner to input event or pain markers,which can be synchronized with the high-resolution data collected by thekinematic implantable device 1002. For example, assume the kinematicimplantable device 1002 is a component in a knee replacement. Themedical practitioner can have the patient walk on a treadmill while thekinematic implantable device 1002 is in the high-resolution mode. As thepatient walks, the patient may complain about pain in his/her knee. Themedical practitioner can click a pain marker button on the doctor officebase station to indicate the patient's discomfort. The doctor officebase station records the marker and the time at which the marker wasinput. When the timing of this marker is synchronized with the timing ofthe collected high-resolution data, the medical practitioner can analyzethe data to try and determine the cause of the pain.

In other embodiments, the doctor office base station (not shown in FIG.3) may provide updated configuration information to the kinematicimplantable device 1002. The kinematic implantable device 1002 can storethis updated configuration information, which can be used to adjust theparameters associated with the collection of the kinematic data. Forexample, if the patient is doing well, the medical practitioner candirect a reduction in the frequency at which the kinematic implantabledevice 1002 collects data. On the contrary, if the patient isexperiencing an unexpected amount of pain, the medical practitioner maydirect the kinematic implantable device 1002 to collect additional datafor a determined period of time (e.g., a few days). The medicalpractitioner may use the additional data to diagnose and treat aparticular problem. In some cases, the additional data may includepersonally descriptive information provided by the patient (not shown inFIG. 3) after the patient has left presence of the medical practitionerand is no longer in range of the doctor office base station. In thesecases, the personally descriptive information may be collected anddelivered from via one or more of the home base station 1004, the smartdevice 1005, and the connected personal assistant 1007. Firmware withinthe kinematic implantable device and/or the base station will providesafeguards limiting the duration of such enhanced monitoring to insurethe battery retains sufficient power to last for the implant'slifecycle.

In various embodiments, the doctor office base station (not shown inFIG. 3) may communicate with a doctor office configuration computingdevice (not shown in FIG. 3). The doctor office configuration computingdevice includes an application with a graphical user interface thatenables the medical practitioner to input commands and data. Some or allof the commands, data, and other information may be later transmitted tothe kinematic implantable device 1002 via the doctor office basestation. For example, in some embodiments, the medical practitioner canuse the graphical user interface to instruct the kinematic implantabledevice 1002 to enter its high-resolution mode. In other embodiments, themedical practitioner can use graphical user interface to input or modifythe configuration information for the kinematic implantable device 1002.The doctor office configuration computing device transmits theinformation (e.g., commands, data, or other information) to the doctoroffice base station via a wired or wireless network connection (e.g.,via a USB connection, Bluetooth connection, or Wi-Fi connection), whichin turn transmits some or all of the information to the kinematicimplantable device 1002.

The doctor office configuration computing device (not shown in FIG. 3)may also display, to the medical practitioner, other informationregarding the kinematic implantable device 1002, regarding the patient(e.g., personally descriptive information), or the doctor office basestation. For example, the doctor office configuration computing devicemay display the high-resolution data that is collected by the kinematicimplantable device 1002 and transmitted to the doctor office basestation (not shown in FIG. 3). The doctor office configuration computingdevice may also display error information if the kinematic implantabledevice 1002 is unable to store or access the configuration information,if the kinematic implantable device 1002 is unresponsive, if thekinematic implantable device 1002 identifies an issue with one of thesensors or radio, if the doctor office base station is unresponsive ormalfunctions, or for other reasons.

In some embodiments, doctor office configuration computing device (notshown in FIG. 3) may have access to the cloud 1008. In at least oneembodiment, the medical practitioner can utilize the doctor officeconfiguration computing device to access data stored in the cloud 1008,which was previously collected by the kinematic implantable device 1002and transmitted to the cloud 1008 via one or both of the home basestation 1004 and smart device 1005. Similarly, the doctor officeconfiguration computing device can transmit the high-resolution dataobtain from the kinematic implantable device 1002 via the doctor officebase station to the cloud 1008. In some embodiments, the doctor officebase station may have internet access and may be enabled to transmit thehigh-resolution data directly to the cloud 1008 without the use of thedoctor office configuration computing device.

In various embodiments, the medical practitioner may update theconfiguration information of the kinematic implantable device 1002 whenthe patient is not in the medical practitioner's office. In these cases,the medical practitioner can utilize the doctor office configurationcomputing device (not shown in FIG. 3) to transmit updated configurationinformation to the kinematic implantable device 1002 via the cloud 1008.One or more of the home base station 1004, the smart device 1005, andthe connected personal assistant 1007 can obtain updated configurationinformation from the cloud 1008 and pass updated configurationinformation to the cloud. This can allow the medical practitioner toremotely adjust the operation of the kinematic implantable device 1002without needing the patient to come to the medical practitioner'soffice. This may also permit the medical practitioner to send messagesto the patient (not shown in FIG. 3) in response, for example, topersonally descriptive information that was provided by the patient andpassed through one or more of the home base station 1004, the smartdevice 1005, and the connected personal assistant 1007 to the doctoroffice base station (not shown in FIG. 3). For example, if a patientwith a knee prosthesis speaks “my leg hurts when I walk” into theconnected personal assistant 1007, then the medical practitioner mayissue a prescription for a pain reliever and cause the connectedpersonal assistant to notify the patient by “speaking” “the doctor hascalled in a prescription for Vicodin® to your preferred pharmacy; theprescription will be ready for pick up at 4 pm.”

Although the doctor office base station (not shown in FIG. 3) and thedoctor office configuration computing device (not shown in FIG. 3) aredescribed as separate devices, embodiments are not so limited; rather,the functionality of the doctor office configuration computing deviceand the doctor office base station may be included in a single computingdevice or in separate devices (as illustrated). In this way, the medicalpractitioner may be enabled in one embodiment to input the configurationinformation or markers directly into the doctor office base station andview the high-resolution data (and synchronized marker information) froma display on the doctor office base station.

Still referring to FIG. 3, alternate embodiments are contemplated. Forexample, one or two of the home base station 1004, the smart device1005, and the connected personal assistant 1007 may be omitted from thekinematic implantable device environment 1000. Furthermore, each of thebase station 1004, the smart device 1005, and the connected personalassistant 1007 may be configured to communicate with one or both of theimplantable device 1002 and the cloud 1008 via another one or two of thebase station, the smart device, and the connected personal assistant.Moreover, the smart device 1005 can be temporarily contracted as aninterface to the implantable prosthesis 1002, and can be any suitabledevice other than a smart phone, such as a smart watch, a smart patch,and any IoT device, such as a coffee pot, capable of acting as aninterface to the implantable device 1002. In addition, one or more ofthe base station 1004, smart device 1005, and connected personalassistant 1007 can act as a communication hub for multiple prosthesesimplanted in one or more patients. Furthermore, one or more of the basestation 1004, smart device 1005, and connected personal assistant 1007can automatically order or reorder prescriptions or medical supplies(e.g., a knee brace) in response to patient input orimplantable-prosthesis input (e.g., pain level, instability level) if amedical professional and insurance company have preauthorized such anorder or reorder; alternatively, one or more of the base station, smartdevice, and connected personal assistant can be configured to request,from a medical professional or an insurance company, authorization toplace the order or reorder. Moreover, one or more of the base station1004, smart device 1005, and connected personal assistant 1007 can beconfigured with a personal assistant such as Alexa® or Siri®. Inaddition, one or more alternate embodiments described below inconjunction with FIGS. 4-27 may be applicable to the kinematicimplantable device environment 1000.

FIG. 4 is a diagram of an implantable circuit 1010, which is configuredfor inclusion within, or otherwise for use with, an alert kinematicimplant such as a knee prothesis implantable as part of a total kneearthroplasty (TKA).

The circuit 1010 is powered by a battery, or other suitable implantablepower source, 1012, and includes a fuse 1014, switches 1016 and 1018, aclock generator and power-management unit 1020, an inertial measurementunit (IMU) 1022, a memory circuit 1024, a radio-frequency (RF)transceiver 1026, an RF filter 1028, an RF-compatible antenna 1030, anda control circuit 1032. Examples of some or all of these components aredescribed elsewhere in this application or in U.S. Ser. No. 16/084,544,which is incorporated by reference in all jurisdictions which allowincorporation by reference.

The battery 1012 can be any suitable battery, such as a Lithium CarbonMonofluoride (LiCFx) battery, or other storage cell configured to storeenergy for powering the circuit 1000 for an expected lifetime (e.g.,5-25+ years) of the kinematic implant.

The fuse 1014 can be any suitable fuse (e.g., permanent) or circuitbreaker (e.g., resettable) configured to prevent the battery 1012, or acurrent flowing from the battery, from injuring the patient and damagingthe battery and one or more components of the circuit 1000. For example,the fuse 1014 can be configured to prevent the battery 1012 fromgenerating enough heat to burn the patient, to damage the circuit 1000,to damage the battery, or to damage structural components of thekinematic implant.

The switch 1016 is configured to couple the battery 1012 to, or touncouple the battery from, the IMU 1022 in response to a control signalfrom the control circuit 1032. For example, the control circuit 1032 maybe configured to generate the control signal having an open state thatcauses the switch 1016 to open, and, therefore, to uncouple power fromthe IMU 1022, during a sleep mode or other low-power mode to save power,and, therefore, to extend the life of the battery 1012. Likewise, thecontrol circuit 1032 also may be configured to generate the controlsignal having a closed state that causes the switch 1016 to close, andtherefore, to couple power to the IMU 1022, upon “awakening” from asleep mode or otherwise exiting another low-power mode. Such a low-powermode may be for only the IMU 1022 or for the IMU and one or more othercomponents of the implantable circuit 1010.

The switch 1018 is configured to couple the battery 1012 to, or touncouple the battery from, the memory circuit 1024 in response to acontrol signal from the control circuit 1032. For example, the controlcircuit 1032 may be configured to generate the control signal having anopen state that causes the switch 1018 to open, and, therefore, touncouple power from the memory 1024, during a sleep mode or otherlow-power mode to save power, and, therefore, to extend the life of thebattery 1012. Likewise, the control circuit 1032 also may be configuredto generate the control signal having a closed state that causes theswitch 1018 to close, and therefore, to couple power to the memory 1024,upon “awakening” from a sleep mode or otherwise exiting anotherlow-power mode. Such a low-power mode may be for only the memory circuit1024 or for the memory circuit and one or more other components of theimplantable circuit 1010.

The clock and power management circuit 1020 can be configured togenerate a clock signal for one or more of the other components of theimplantable circuit 1010, and can be configured to generate periodiccommands or other signals (e.g., interrupt requests) in response towhich the control circuit 1032 causes one or more components of theimplantable circuit to enter or to exit a sleep, or other low-power,mode. The clock and power management circuit 1020 also can be configuredto regulate the voltage from the battery 1012, and to provide a regulatepower-supply voltage to some or all of the other components of theimplantable circuit 1010.

The IMU 1022 has a frame of reference with coordinate x, y, and z axes,and can be configured to measure, or to otherwise quantify, accelerationthat the IMU experiences along each of the x, y, and z axes, and angularvelocity that the IMU experiences about each of the x, y, and z axes.Such a configuration of the IMU 1022 is at least a six-axisconfiguration, because the IMU 1022 measures six unique quantities,acc_(x)(t), acc_(y)(t), acc_(z)(t), Ω_(x)(t), Ω_(y)(t), and Ω_(z)(t).Alternatively, the IMU 1022 can be configured in a nine-axisconfiguration, in which the IMU can use gravity to compensate for, or tootherwise correct for, accumulated errors in acc_(x)(t), acc_(y)(t),acc_(z)(t), Ω_(x)(t), Ω_(y)(t), and Ω_(z)(t). But in an embodiment inwhich the IMU measures acceleration and angular velocity over only shortbursts (e.g., 0.10-100 seconds(s)), for many applications accumulatederror typically can be ignored without exceeding respective errortolerances. The IMU 1022 can include a respective analog-to-digitalconverter (ADC) for each of the x, y, and z accelerometers andgyroscopes. Alternatively, the IMU 1022 can include a respectivesample-and-hold circuit for each of the x, y, and z accelerometers andgyroscopes, and as few as one ADC that is shared by the accelerometersand gyroscopes. Including fewer than one ADC per accelerometer andgyroscope can decrease one or both of the size and circuit density ofthe IMU 1022, and can reduce the power consumption of the IMU. Butbecause the IMU 1022 includes a respective sample-and-hold circuit foreach accelerometer and each gyroscope, samples of the analog signalsgenerated by the accelerometers and the gyroscopes can be taken at thesame or different sample times, at the same or different sample rates,and with the same or different output data rates (ODR).

The memory circuit 1024 can be any suitable nonvolatile memory circuit,such as EEPROM or FLASH memory, and can be configured to store datawritten by the control circuit 1032, and to provide data in response toa read command from the control circuit.

The RF transceiver 1026 can be a conventional transceiver that isconfigured to allow the control circuit 1032 (and optionally the fuse1014) to communicate with a base station (not shown in FIG. 4)configured for use with the kinematic implantable device. For example,the RF transceiver 1026 can be any suitable type of transceiver (e.g.,Bluetooth, Bluetooth Low Energy (BTLE), and WiFi®), can be configuredfor operation according to any suitable protocol (e.g., MICS, ISM,Bluetooth, Bluetooth Low Energy (BTLE), and WiFi®), and can beconfigured for operation in a frequency band that is within a range of 1MHz-5.4 GHz, or that is within any other suitable range.

The filter 1028 can be any suitable bandpass filter, such as a surfaceacoustic wave (SAW) filter or a bulk acoustic wave (BAW) filter.

The antenna 1030 can be any antenna suitable for the frequency band inwhich the RF transceiver 1026 generates signals for transmission by theantenna, and for the frequency band in which a base station (not shownin FIG. 4) generates signals for reception by the antenna.

The control circuit 1032, which can be any suitable implantablereporting processor (IRP) such as a microcontroller or microprocessor,is configured to control the configuration and operation of one or moreof the other components of the implantable circuit 1010. For example,the control circuit 1032 is configured to control the IMU 1022 to takemeasurements of movement of the implantable prosthesis with which theimplantable circuit 1010 is associated, to quantify the quality of suchmeasurements (e.g., is the measurement “good” or “bad”), to store, inthe memory 1024, measurement data generated by the IMU, to generatemessages include the stored data as a payload, to packetize themessages, to provide the message packets to the RF transceiver 1026 fortransmission to the base station (not shown in FIG. 4). The controlcircuit 1032 also can be configured to execute commands received from abase station (not shown in FIG. 4) via the antenna 1030, filter 1028,and RF transceiver 1026. For example, the control circuit 1032 can beconfigured to receive configuration data from the base station, and toprovide the configuration data to the component of the implantablecircuit 1010 to which the base station directed the configuration data.If the base station directed the configuration data to the controlcircuit 1032, then the control circuit is configured to configure itselfin response to the configuration data.

Still referring to FIG. 4, operation of the circuit 1010 is described,according to an embodiment in which an implantable prosthesis in whichthe circuit is disposed, or with which the circuit is otherwiseassociated, is implanted in a patient (not shown in FIG. 4).

The fuse 1014, which is normally electrical closed, is configured toopen electrically in response to an event that can injure the patient inwhich the implantable circuit 1010 resides, or damage the battery 1012of the implantable circuit, if the event persists for more than a safelength of time. An event in response to which the fuse 1014 can openelectrically includes an overcurrent condition, an overvoltagecondition, an overtemperature condition, an over-current-time condition,and over-voltage-time condition, and an over-temperature-time condition.An overcurrent condition occurs in response to a current through thefuse 1014 exceeding an overcurrent threshold. Likewise, an overvoltagecondition occurs in response to a voltage across the fuse 1014 exceedingan overvoltage threshold, and an overtemperature condition occurs inresponse to a temperature of the fuse exceeding a temperature threshold.An over-current-time condition occurs in response to an integration of acurrent through the fuse 1014 over a measurement time window (e.g., tenseconds) exceeding a current-time threshold, where the window can“slide” forward in time such that the window always extends from thepresent time back the length, in units of time, of the window.Alternatively, an over-current-time condition occurs if the currentthrough the fuse 1014 exceeds an overcurrent threshold for more than athreshold time. Similarly, an over-voltage-time condition occurs inresponse to an integration of a voltage across the fuse 1014 over ameasurement time window, and an over-temperature-time condition occursin response to an integration of a temperature of the fuse over ameasurement time window. Alternatively, an over-voltage-time conditionoccurs if the voltage across the fuse 1014 exceeds an overvoltagethreshold for more than a threshold time, and an over-temperature-timecondition occurs if a temperature associated with the fuse 1014, battery1012, or implantable circuit 1010 exceeds an overtemperature thresholdfor more than a threshold time. But even if the fuse 1014 opens, thusuncoupling power from the implantable circuit 1010, the mechanical andstructural components of the kinematic prosthesis (not shown in FIG. 4)with which the implantable circuit is associated are still fullyoperational. For example, if the kinematic prosthesis is a kneeprosthesis, then the knee prosthesis still can function fully as apatient's knee; abilities lost, however, are the abilities to detect andto measure kinematic motion of the prosthesis, to generate and to storedata representative of the measured kinematic motion, and to provide thestored data to a base station or other destination external to thekinematic prosthesis. Operation of the fuse is further described belowin conjunction with FIG. 27.

The control circuit 1032 is configured to cause the IMU 1022 to measure,in response to a movement of the kinematic prosthesis with which theimplantable circuit 1010 is associated, the movement over a window oftime (e.g., ten seconds, twenty seconds, one minute), to determine ifthe measured movement is a qualified movement, to store the datarepresentative of a measured qualified movement, and to cause the RFtransceiver 1026 to transmit the stored data to a base station or othersource external to the prosthesis.

For example, the IMU 1022 can be configured to begin sampling the sensesignals output from its one or more accelerometers and one or moregyroscopes in response to a detected movement within a respective timeperiod (day), and the control circuit 1032 can analyze the samples todetermine if the detected movement is a qualified movement. Further inexample, the IMU 1022 can detect movement in any conventional manner,such as by movement of one or more of its one or more accelerometers. Inresponse to the IMU 1022 notifying the control circuit 1032 of thedetected movement, the control circuit can correlate the samples fromthe IMU to stored accelerator and gyroscope samples generated with acomputer simulation or while the patient, or another patient, is walkingnormally, and can measure the time over which the movement persists (thetime equals the number of samples times the inverse of the samplingrate). If the samples of the accelerator and gyroscope output signalscorrelate with the respective stored samples, and the time over whichthe movement persists is greater than a threshold time, then the controlcircuit 1032 effectively labels the movement as a qualified movement.

In response to determining that the movement is a qualified movement,the control circuit 1032 stores the samples, along with other data, inthe memory circuit 1024, and may disable the IMU 1022 until the nexttime period (e.g., the next day or the next week) by opening the switch1016 to extend the life of the battery 1012. The clock and powermanagement circuit 1020 can be configured to generate periodic timingsignals, such as interrupts, to commence each time period. For example,the control circuit 1032 can close the switch 1016 in response to such atiming signal from the clock and power management circuit 1020.Furthermore, the other data can include, e.g., the respective samplerate for each set of accelerometer and gyroscope samples, a respectivetime stamps indicating the time at which the IMU 1022 acquired thecorresponding sets of samples, the respective sample times for each setof samples, an identifier (e.g., serial number) of the implantableprosthesis, and a patient identifier (e.g., a number or name). Thevolume of the other data can be significantly reduced if the samplerate, time stamp, and sample time are the same for each set of samples(i.e., samples of signals from all accelerometers and gyroscopes takenat the same times at the same rate) because the header includes only onesample rate, one time stamp, and one set of sample times for all sets ofsamples. Furthermore, the control circuit 1032 can encrypt some or allof the data in a conventional manner before storing the data in thememory 1024. For example, the control circuit 1032 can encrypt some orall of the data dynamically such that at any given time, same data has adifferent encrypted form than if encrypted at another time.

As further described below in conjunction with FIGS. 9-24 and elsewherein this application, the stored data samples of the signals that the IMU1022 one or more accelerometers and one or more gyroscopes generate canprovide clues to the condition of the implantable prosthesis. Forexample, one can analyze the data samples (e.g., with a remote serversuch as a cloud server) to determine whether a surgeon implanted theprosthesis correctly, to determine the level(s) of instability anddegradation that the implanted prosthesis exhibits at present, todetermine the instability and degradation profiles over time, and tocompare the instability and degradation profiles to benchmarkinstability and degradation profiles developed with stochasticsimulation or data from a statistically significant group of patients.

Furthermore, the sampling rate, output data rate (ODR), and samplingfrequency of the IMU 1022 can be configured to any suitable values. Forexample, the sampling rate may be fixed to any suitable value such as at3200 Hz, the ODR, which can be no greater than the sampling rate andwhich is generated by “dropping” samples periodically, can be anysuitable value such as 800 Hz, and the sampling frequency (the inverseof the interval between sampling periods) for qualified events can beany suitable value, such as twice per day, once per day, once per every2 days, once per week, once per month, or more or less frequently. Andsampling rate or ODR can be varied depending on the type of event beingsampled. For example, to detect that the patient is walking withoutanalyzing the patient's gait or the implant for instability or wear, thesampling rate or ODR can be 200 Hz, 25 Hz, or less. Therefore, such alow-resolution mode can be used to detect a precursor (a patient takingsteps with a knee prosthesis) to a qualified event (a patient taking atleast ten consecutive steps) because a “search” for a qualified eventmay include multiple false detections before the qualified even isdetected. By using a lower sampling rate or ODR, the IMU 1032 savespower while conducting the search, and increases the sampling rate orthe ODR (e.g., to 800 Hz, 1600, or 3200 Hz) only for sampling a detectedqualified event so that the accelerator and gyroscope signals havesufficient sampling resolution for analysis of the samples for, e.g.,instability and wear of the prosthesis.

Still referring to FIG. 4, in response to being polled by a base station(not shown in FIG. 4) or by another device external to the implantedprosthesis, the control circuit 1032 generates conventional messageshaving payloads and headers. The payloads include the stored samples ofthe signals that the IMU 1022 accelerators and gyroscopes generated, andthe headers include the sample partitions in the payload (i.e., in whatbit locations the samples of the x-axis accelerometer are located, inwhat bit locations the samples of the x-axis gyroscope are located,etc.), the respective sample rate for each set of accelerometer andgyroscope samples, a time stamp indicating the time at which the IMU1022 acquired the samples, an identifier (e.g., serial number) of theimplantable prosthesis, and a patient identifier (e.g., a number orname).

The control circuit 1032 generates data packets that include themessages according to a conventional data-packetizing protocol. Eachpacket can also include a packet header that includes, for example, asequence number of the packet so that the receiving device can order thepackets properly even if the packets are transmitted or received out oforder.

The control circuit 1032 encrypts some or all parts of each of the datapackets, for example, according to a conventional encryption algorithm,and error encodes the encrypted data packets. For example, the controlcircuit 1032 encrypts at least the prosthesis and patient identifiers torender the data packets compliant with the Health Insurance Portabilityand Accountability Act (HIPAA).

The control circuit 1032 provides the encrypted and error-encoded datapackets to the RF transceiver 1026, which, via the filter 1028 andantenna 1030, transmits the data packets to a destination, such as thebase station 1004 (FIG. 3), external to the implantable prothesis. TheRF transceiver 1026 can transmit the data packets according to anysuitable data-packet-transmission protocol.

Still referring to FIG. 4, alternate embodiments of the implantablecircuit 1010 are contemplated. For example, the RF transceiver canperform encryption or error encoding instead of, or complementary to,the control circuit 1032. Furthermore, one or both of the switches 1016and 1018 can be omitted from the implantable circuit 1010. Moreover, theimplantable circuit 1010 can include components other than thosedescribed herein and can omit one or more of the components describedherein. In addition, one or more embodiments described in conjunctionwith FIG. 3 and FIGS. 5-27 may be applicable to the implantable circuit1010.

FIG. 5 is a diagram of a base-station circuit 1040, which is configuredfor inclusion within, or otherwise for use with, a base station, such asthe home base station 1004 of FIG. 3, configured for communication withthe implantable circuit 100 of FIG. 4, according to an embodiment.

The base-station circuit 1040 is powered by a power supply 1042, andincludes first and second antennas 1044 and 1046, first and second RFfilters 1048 and 1050, first and second RF transceivers 1052 and 1054, amemory circuit 1056, and a base-station control circuit 1058. Examplesof some or all of these components are described elsewhere in thisapplication or in U.S. patent application Ser. No. 16/084,544, which isincorporated by reference in all jurisdictions which allow incorporationby reference.

The power supply 1042 can be any suitable power supply, such as abattery or a supply that receives power from an electrical outlet; ifthe power supply is of the latter type, then the power supply also caninclude a battery backup for power outages or for while the base-stationcircuit 1040 is “unplugged.”

The antenna 1044 can be any antenna suitable for the frequency band inwhich the RF transceiver 1052 communicates with the implant circuit 1010of FIG. 4.

Likewise, the antenna 1046 can be any antenna suitable for the frequencyband in which the RF transceiver 1054 communicates with a component,e.g., a WiFi® router, access point, or repeater, of the home network1006 of FIG. 3.

Each of the filters 1048 and 1050 can be any suitable bandpass filter,such as a surface acoustic wave (SAW) filter or a bulk acoustic wave(BAW) filter.

The RF transceiver 1052 can be a conventional transceiver that isconfigured to allow the control circuit 1058 to communicate with theimplant circuit 1010 of FIG. 4 while the implant circuit is disposedwithin, or is otherwise associated with, an implantable prosthesis suchas the kinematic implantable device 1002 of FIG. 3. For example, the RFtransceiver 1052 can be any suitable type of transceiver (e.g.,Bluetooth, Bluetooth Low Energy (BTLE), and WiFi®), can be configuredfor operation according to any suitable protocol (e.g., M ICS, ISM,Bluetooth, Bluetooth Low Energy (BTLE), and WiFi®), and can beconfigured for operation in a frequency band that is within a range of 1MHz-5.4 GHz, or that is within any other suitable range.

Likewise, the RF transceiver 1054 can be any conventional transceiverthat is configured to allow the control circuit 1058 to communicate witha component, e.g., a WiFi® router, access point, or repeater, of thehome network 1006 of FIG. 3, or with one or more of the home basestation 1004, the smart device 1005, and the connected personalassistant 1000 of FIG. 3. For example, the RF transceiver 1026 can beany suitable type of transceiver (e.g., Bluetooth, Bluetooth Low Energy(BTLE), and WiFi®), can be configured for operation according to anysuitable protocol (e.g., MICS, ISM, Bluetooth, Bluetooth Low Energy(BTLE), and WiFi®), and can be configured for operation in a frequencyband that is within a range of 1 MHz-5.4 GHz, or that is within anyother suitable range.

The memory circuit 1056 can be any suitable nonvolatile memory circuit,such as EEPROM or FLASH memory, and can be configured to store datawritten by the control circuit 1058, and to provide data in response toa read command from the control circuit. For example, the controlcircuit 1058 can store, in the memory 1056, data packets received fromthe implantable circuitry 1010 of FIG. 5, and can store data packetsreceived from a cloud server via the RF transceiver 1054, where the datapackets include, for example, commands, instructions, or configurationdata for the implantable circuit 1010 of FIG. 4. Alternatively, thememory 1056 can include volatile memory.

The base-station control circuit 1058, which can be any suitableprocessor such as a microcontroller or microprocessor, is configured tocontrol the configuration and operation itself and of one or more of theother components of the base-station circuit 1040. For example, thebase-station control circuit 1058 can be configured to receive datapackets from the implantable circuit 1010 of FIG. 4 via the RFtransceiver 1052, to convert the received data packets into data packetssuitable for transmission to the home network 1006 of FIG. 3, and totransmit the converted data packets to the home network via the RFtransceiver 1054. And the base-station control circuit 1058 also can beconfigured to receive data packets from the home network 1006 via the RFtransceiver 1054, to convert the received data packets into data packetssuitable for transmission to the implantable circuit 1010, and totransmit the converted data packets to the implantable circuit via theRF transceiver 1052.

Still referring to FIG. 5, operation of the base-station circuit 1040 isdescribed, according to an embodiment in which an implantable prosthesis(not shown in FIG. 5) with which the base-station circuit communicatesis implanted in a patient (not shown in FIG. 5).

The control circuit 1058 polls the implantable circuit 1010 (FIG. 4) ofthe implanted prosthesis (not shown in FIG. 5) at regular intervals,such as once per day, once every other day, once per week, or once permonth. If the control circuit 1058 receives no response to a poll, thenthe control circuit may poll the implantable circuit 1010 morefrequently (e.g., every 5 minutes, every 30 minutes, every hour) untilit receives a response or determines that the implanted prosthesis isout of range of the base station.

The implantable circuit 1010 (FIG. 4) responds to a poll by transmittingall the data packets of IMU samples that the implantable circuitgenerated since the last transmission of data packets.

The antenna RF transceiver 1052 receives the data packets from theimplantable circuit 1010 (FIG. 4) via the antenna 1044 and filter 1048,and provides the received data packets to the base-station controlcircuit 1058, which decodes and decrypts the data packets, parses themessages from the data packets, and stores the parsed messages in thememory circuit 1056. Before storing the parsed messages, thebase-station control circuit 1058 may encrypt part of all of each of theparsed messages for compliance with HIPAA.

Then, the base-station control circuit 1058 reformats the storedmessages, or generates new messages in response to the headers andpayloads of the stored messages. For example, the base-station controlcircuit 1058 may generate new messages that each include a respectivepayload and header from a received message, but that each includeadditional header information such as an identifier of the base station1004 (FIG. 4), a time of reception of the original message from theimplantable circuit 1010 (FIG. 4), and time of generation of the newmessage.

Before generating the new messages, the base-station control circuit1058 may decrypt the parsed messages stored in the memory 1056.

The base-station control circuit 1058 then generates data packets thatinclude the new messages, encrypts part or all of each of the datapackets, and error encodes the data packets, and provides the encryptedand encoded data packets to the RF transceiver 1054, which transmits theencrypted and encoded data packets to the home network 1006 via thefilter 1050 and the antenna 1046. The base-station control circuit 1058may store the encrypted and encoded data packets in the memory 1056temporarily (e.g., in a buffer) before providing the data packets to theRF transceiver 1054.

In an alternative embodiment, the base-station control circuit 1058“passes through” the data packets received from the implantable circuit1010 (FIG. 4) to the home network 1006 (FIG. 3). That is, thebase-station control circuit 1058 receives one or more data packets fromthe implantable circuit 1010 via the RF transceiver 1052, temporarilystores the one or more data packets in the memory 1056, and causes theRF transceiver 1054 to transmit the one or more data packets to the homenetwork 1006.

In yet another alternative, the control circuit 1058 modifies the one ormore data packets received from the implantable circuit 1010 (FIG. 4)without first parsing the one or more data packets, or with parsingsome, but not all, of each data packet.

The home network 1006 (FIG. 3) may “pass through” the one or more datapackets received from the base station 1004 to a destination such as aserver on the cloud 1008 (FIG. 3), or may modify the one or more datapackets according to a suitable communication protocol before sendingthe one or more data packets to the destination.

Operation of the base-station circuit 1040 is described further inconjunction with FIG. 26.

Still referring to FIG. 5, alternate embodiments of the base-stationcircuit 1040 are contemplated. For example, embodiments described inconjunction with FIGS. 3-4 and 6-27 may be applicable to thebase-station circuit 1040.

FIG. 6 is a perspective view of the IMU 1022 of FIG. 4, according to anembodiment. For example, the IMU 1022 can be a Bosch BMI 160 small,low-power, IMU.

As described above in conjunction with FIG. 4, the IMU 1022 includesthree measurement axes 1060, 1062, and 1064, which, for purposes ofdescription, are arbitrarily labeled x, y, z. That is, in a Cartesiancoordinate system, the labels “x,” “y,” and “z” can be appliedarbitrarily to the axes 1060, 1062, and 1064 in any order orarrangement. A mark 1066 is a reference that indicates the locations andorientations of the axes 1060, 1062, and 1064 relative to the IMU 1022package.

The IMU 1022 includes three accelerometers (not shown in FIG. 6), eachof which senses and measures an acceleration a(t) along a respective oneof the axes 1060 (x), 1062 (y), and 1064 (z), where a_(x)(t) is theacceleration along the x axis, a_(y)(t) is the acceleration along the yaxis, and a_(z)(t) is the acceleration along the z axis. Eachaccelerometer generates a respective analog sense or output signalhaving an instantaneous magnitude that represents the instantaneousmagnitude of the sensed acceleration along the corresponding axis. Forexample, the magnitude of the magnitude of the accelerometer outputsignal at a given time is proportional the magnitude of the accelerationalong the accelerometer's sense axis at the same time.

The IMU 1022 also includes three gyroscopes (not shown in FIG. 6), eachof which senses and measures angular velocity Ω(t) about a respectiveone of the axes 1060 (x), 1062 (y), and 1064 (z), where Ω_(x)(t) is theangular velocity along the x axis, Ω_(y)(t) is the angular velocityalong the y axis, and Ω_(z)(t) is the angular velocity along the z axis.Each gyroscope generates a respective analog sense or output signalhaving an instantaneous magnitude that represents the instantaneousmagnitude of the sensed angular velocity about the corresponding axis.For example, the magnitude of the gyroscope output signal at a giventime is proportional the magnitude of the angular velocity about thegyroscope's sense axis at the same time.

The IMU 1022 includes at least two analog-to-digital converters (ADCs)(not shown in FIG. 6) for each axis 1060, 1062, and 1064, one ADC forconverting the output signal of the corresponding accelerometer into acorresponding digital acceleration signal, and the other ADC forconverting the output signal of the corresponding gyroscope into acorresponding digital angular-velocity signal. For example, each of theADCs may be an 8-bit, 16-bit, or 24-bit ADC.

A circuit designer can configure each ADC (not shown in FIG. 6) to haverespective parameter values that are the same as, or that are differentfrom, the parameter values of the other ADCs. Examples of suchparameters having settable values include sampling rate, dynamic rangeat the ADC input node(s), and output data rate (ODR). One or more ofthese parameters may be set to a constant value, while one or moreothers of these parameters may be settable dynamically (e.g., during runtime). For example, the respective sampling rate of each ADC may besettable dynamically so that during one sampling period the samplingrate has one value and during another sampling period the sampling ratehas another value.

For each digital acceleration signal and for each digitalangular-velocity signal, the IMU 1022 can be configured to provide theparameter values associated with the signal. For example, the IMU 1022can provide, for each digital acceleration signal and for each digitalangular-velocity signal, the sampling rate, the dynamic range, and atime stamp indicating the time at which the first sample or the lastsample was taken. The IMU 1022 can be configured to provide theseparameter values in the form of a message header (the correspondingsamples form the message payload) or in any other suitable form.

Still referring to FIG. 6, alternate embodiments of the IMU 1022 arecontemplated. For example, the IMU 1022 can have a shape other thansquare or rectangular. Furthermore, embodiments described in conjunctionwith FIGS. 3-5 and 7-27 may be applicable to the IMU 1022.

FIG. 7 is a front view of a standing male patient 1070 with a kneeprosthesis 1072 implanted to replace his left knee joint, and of theaxes 1060, 1062, and 1064 (arbitrarily labeled x, y, and z) of the IMU1022 (FIG. 6), according to an embodiment.

FIG. 8 is a side view of the patient 1070 of FIG. 7 in a supineposition, and of the axes 1060, 1062, and 1064 (arbitrarily labeled x,y, and z) of the IMU 1022 (FIG. 6), according to an embodiment (the kneeprosthesis 1072 is shown through the patient's right leg).

Referring to FIGS. 7-8, in an embodiment, ideally one IMU axis (the xaxis 1060 in FIGS. 7-8) is vertical while the patient 1070 is standingstraight, one IMU axis (the y axis 1062 in FIGS. 7-8) that is, or thatis parallel to, the axis about which the knee prosthesis rotates orbends, and the remaining IMU axis (the z axis 1064 in FIGS. 7-8)perpendicular to the other two axes, where all three axes intersect atthe origin of the coordinate system.

There are a number of techniques that aid the surgeon who implants theknee prosthesis 1072 to align the IMU axes 1060, 1062, and 1064 with theideal axis orientational. First, the orientation of the IMU 1022 (FIG.6) within the tibial extension (described elsewhere in this document) isfixed within a relatively tight tolerance from extension to extensionduring the process of assembling the tibial extension by the physicaldesign of the components. Second, both the tibial extension and thetibial baseplate (described elsewhere in this document) includealignment markers that the surgeon uses to align the tibial extensionwith the tibial baseplate component during the procedure for implantingthe knee prosthesis such that the extension-plate alignment is within arelatively tight tolerance from implant to implant. Third, theuniformity of the tibial head from patient to patient, and theuniformity of how the surgeon modifies the tibial head for accepting thetibial baseplate, fixes the orientation of the tibial baseplatecomponent within a relatively tight tolerance from patient to patient.

Despite these axis-alignment techniques, the IMU axes 1060, 1062, and1064 may be misaligned relative to the ideal axis alignments describedabove. For example, such misalignment can have one or both of atranslational component and a rotational component, although therotational component is typically more prominent than the translationalcomponent. Further in example, the rotational misalignment can rangefrom approximately a fraction of degree to approximately 90°.

Techniques for compensating for, or correcting, such axis misalignmentare described elsewhere in this patent application.

Still referring to FIGS. 7-8, alternate embodiments of the describedaxis orientation and axis-orientation techniques are contemplated. Forexample, the described axis orientation can be modified for other typesof implanted prostheses, such as shoulder prosthesis and hip prostheses.Furthermore, embodiments described in conjunction with FIGS. 3-6 andFIGS. 9-27 may be applicable to the described axis orientation andaxis-orientation techniques.

FIG. 9 is a plot 1080, versus time, of the digitized versions of theanalog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) (in unitsof m/s²) that the accelerometers of the IMU 1022 (FIG. 4) respectivelygenerate in response to accelerations along the x axis 1060, the y axis1062, and the z axis 1064 (FIG. 6) while the patient 1070 (FIGS. 7-8) iswalking forward with a normal gait for a period of about ten seconds,according to an embodiment. In the described example, the x, y, and zaxes have the ideal alignment described in conjunction with FIGS. 7-8,the knee prosthesis 1072 (FIGS. 7-8) exhibits little or no instabilityor wear-induced degradation, and the IMU 1022 samples each of the analogacceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) at the same sampletimes, the sampling rate is 3200 Hz, and the output data rate (ODR) is800 Hz. The ODR is the rate of the samples output by the IMU 1022 and isgenerated by down sampling the samples taken at 3200 Hz. That is,because 3200 Hz/800 Hz=4, the IMU 1022 generates an 800 Hz ODR byoutputting only every fourth sample taken at 3200 Hz.

FIG. 10 is a plot 1082, versus time, of the digitized versions of theanalog angular-velocity signals Ω_(x)(t), Ω_(y)(t), and Ω_(z)(t) (inunits of degrees/s) that the gyroscopes of the IMU 1022 (FIG. 4)respectively generate in response to angular velocities about the x axis1060, the y axis 1062, and the z axis 1064 (FIG. 6) while the patient1070 (FIGS. 7-8) is walking forward with a normal gait for a period ofabout ten seconds, according to an embodiment. In the described example,the x, y, and z axes have the ideal alignment described in conjunctionwith FIGS. 7-8, the knee prosthesis 1072 (FIGS. 7-8) exhibits little orno instability or wear-induced degradation, and the IMU 1022 sampleseach of the analog angular-velocity signals Ω_(x)(t), Ω_(y)(t), andΩ_(z)(t) and each of the analog acceleration signals a_(x)(t), a_(y)(t),and a_(z)(t) at the same sample times and at the same sampling rate of3200 Hz and ODR of 800 Hz. That is, the plot 1082 is aligned, in time,with the plot 1080 of FIG. 9.

FIG. 11 is a middle portion 1084 of the plot 1080 of FIG. 9 with anexpanded (i.e., higher-resolution) time scale and with walk-relatedevents marked, according to an embodiment. For example, the times atwhich the heel of the patient 1070 (FIGS. 7-8) strikes the surface onwhich he is walking, and the times at which the patients lifts his toeoff from the surface, are marked. Furthermore, the middle portion 1084excludes the beginning portion of the plot 1080, which beginning portionrepresents the period during which the patient 1070 is accelerating tohis normal walking speed, excludes the ending portion of the plot 1080,which ending portion represents the period during which the patient isdecelerating to a stop, and, therefore, represents the period duringwhich the patient is walking at an approximately constant velocity.

FIG. 12 is a middle portion 1086 of the plot 1082 of FIG. 10 with thesame expanded (i.e., higher-resolution) time scale as the plot 1084 ofFIG. 11, according to an embodiment. For example, the times at which theheel of the patient 1070 (FIGS. 7-8) strikes the surface on which he iswalking, the times at which the patients lifts his toe off from thesurface, and the times of peak angular velocity Ω_(y)(t) of the kneeprosthesis as it bends about the y axis (or about an axis that isapproximately parallel to the y axis), are marked. Furthermore, themiddle portion 1086 excludes the beginning portion of the plot 1082,which beginning portion represents the period during which the patient1070 is accelerating to his normal walking speed, excludes the endingportion of the plot 1082, which ending portion represents the periodduring which the patient is decelerating to a stop, and, therefore,represents the period during which the patient is walking at anapproximately constant velocity.

Referring to FIGS. 4 and 9-12, the implantable control circuit 1032 canbe configured to determine whether the patient 1070 is walking bycomparing the acceleration and angular-velocity signals generated by theaccelerometers and gyroscopes of the IMU 1020 to benchmark normal-gaitsignals such as those shown in the plots 1080, 1082, 1084, and 1086. Forexample, the implantable control circuit 1032 can be configured tocorrelate the digitized acceleration signals a_(x)(t), a_(y)(t), anda_(z)(t) and the digitized angular-velocity signals Ω_(x)(t), Ω_(y)(t),and Ω_(z)(t) generated by the accelerometers, gyroscopes, and ADCs ofthe IMU 1020 with the respective benchmark normal-gait signals, and candetermine that the patient 1070 is walking if the correlation yields acorrelation value greater than a correlation threshold, which can have avalue, for example, in an approximate range of 0.60-0.95 (1.0 is themaximum value that the correlation can yield). Alternatively, to saveprocessing power and time, the implantable control circuit 1032 can beconfigured to correlate regions of the acceleration and angular-velocitysignals generated by the accelerometers and gyroscopes of the IMU 1020to regions, such as the heel-strike regions, of the benchmarknormal-gait signals. And determining that the patient 1070 is walking isone of one or more determinations that the implantable control circuit1032 can be configured to make to determine whether the acceleration andangular-velocity signals from the IMU 1020 are qualified signals thatthat implantable control circuit 1032 is configured to store. Therespective benchmark normal-gait signals can be generated by the patient1070 himself, for example in a doctor's office (the doctor can controlthe implantable control circuit 1032 to store the benchmark normal-gaitsignals in the memory circuit 1024). Or, the respective benchmarknormal-gait signals can be generated by simulation of the normal gait ofthe patient 1070, or in response to a statistical analysis of the normalgaits of a group of other patients with the same or similar kneeprosthesis. If the respective benchmark normal-gait signals aregenerated in response to other than the actual gait of the patient 1070himself, then, during the correlation, the implantable control circuit1032 can expand or contract the benchmark normal-gait signals in thetime or magnitude dimensions to account for the stride of the patient1070. For example, the taller the patient 1070, the longer his stride;conversely, the shorter the patient 1070 the shorter his stride.

Still referring to FIGS. 9-12, alternate embodiments of the describedbenchmark-signal generation techniques and signal-comparison techniquesare contemplated. For example, embodiments described in conjunction withFIGS. 3-8 and 13-27 may be applicable to the signals and techniquesdescribed in conjunction with FIGS. 9-12.

FIG. 13 is a plot 1090, versus time, of the digitized versions of theanalog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) that theaccelerometers of the IMU 1022 (FIG. 4) respectively generate inresponse to accelerations along the x axis 1060, the y axis 1062, andthe z axis 1064 (FIG. 6) during one of the heel strikes described abovein conjunction with FIGS. 9-12 while the patient 1070 is walking forwardwith a normal gait, according to an embodiment. In the describedexample, the x, y, and z axes have the ideal alignment described inconjunction with FIGS. 7-8, the knee prosthesis 1072 (FIGS. 7-8)exhibits little or no instability or wear-induced degradation, and theIMU 1022 samples each of the analog acceleration signals a_(x)(t),a_(y)(t), and a_(z)(t) at the same sample times, and the effectivesampling rate is 800 Hz. For example, because, for a knee prosthesis,weight is transferred to the prosthetic joint during a heel strike,heel-strike regions can be good regions of a gait signal to analyze forinstability and wear of the prosthesis.

FIG. 14 is a plot 1092, versus frequency, of the respective spectraldistributions X(f), Y(f), and Z(f) of the x, y, and z accelerationsrepresented by the digitized versions of the analog acceleration signalsa_(x)(t), a_(y)(t), and a_(z)(t) of FIG. 13, according to an embodiment.For example, a server (e.g., a cloud server) remote from the kneeprosthesis can generate the spectral distributions X(f), Y(f), and Z(f)by taking the Discrete Fourier Transform (DFT), or (Fast FourierTransform (FFT)), of each of the digitized versions of the analogacceleration signals a_(x)(t), a_(y)(t), and a_(z)(t). Althoughdescribed as having arbitrary units, the spectral distributions X(f),Y(f), and Z(f) can be mathematically manipulated to have any suitableunits such as units of, e.g., energy (Joules, Joules Root Mean Square).

FIG. 15 is a plot 1094, versus frequency, of the cumulative spectraldistributions XYZ(f) (e.g., in units of Joules Root Mean Square,logarithmic scale, or otherwise in arbitrary units) of the x, y, and zaccelerations represented by the digitized versions of the analogacceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) of FIG. 13,according to an embodiment. For example, a server (e.g., a cloud server)remote from the knee prosthesis can generate the cumulative spectraldistribution by integrating each of the contents X(f), Y(f), and Z(f)(FIG. 14) over time and by summing together the respective integrationresults.

Referring to FIGS. 13-15, one can use the spectral distributions X(f),Y(f), and Z(f), and the cumulative spectral distribution XYZ(f), asbenchmarks for determining whether the knee prosthesis 1072 exhibitsinstability or wear-induced degradation. For example, analysis of thecumulative spectral distribution XYZ(f) shows that for a knee prosthesis1072 that exhibits no instability or degradation, approximately 90% ofthe RMS motion is at frequencies of less than 10 Hz, and approximately98% of the RMS motion is at frequencies less than 20 Hz. Therefore, ifthe cumulative spectral distribution XYZ(f) were to yield significantRMS motion above 20 Hz, then this would be an indication that the kneeprosthesis 1072 may be exhibiting instability or degradation.

The respective benchmark analog acceleration signals a_(x)(t), a_(y)(t),and a_(z)(t), in response to which the benchmark spectral distributionsX(f), Y(f), and Z(f) and the benchmark cumulative spectral distributionXYZ(f) are generated, can be generated by the patient 1070 himself, forexample in a doctor's office (the doctor can control the implantablecontrol circuit 1032 to store the benchmarknormal-gait-no-instability-and-no-degradation signals in the memorycircuit 1024). Or, the respective benchmarknormal-gait-no-instability-and-no-degradation signals can be generatedby simulation of the normal gait of the patient 1070, or in response toa statistical analysis of the normal gaits of a group of other patientswith the same or similar knee prosthesis.

Still referring to FIGS. 13-15, alternate embodiments of the describedbenchmark-signal, spectral-distribution, andcumulative-spectral-distribution generation techniques and analysistechniques are contemplated. For example, the sampling rate and the ODRthat the IMU 1022 implements to generate the described benchmark signalmay be other than 3200 Hz and 800 Hz, respectively. Moreover,embodiments described in conjunction with FIGS. 3-12 and 16-27 may beapplicable to the signals, spectral distributions, cumulative spectraldistributions, and techniques described in conjunction with FIGS. 13-15.

FIG. 16 is a plot 1096, versus time, of the digitized versions of theanalog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) (in unitsof m/s²) that the accelerometers of the IMU 1022 (FIG. 4) respectivelygenerate in response to accelerations along the x axis 1060, the y axis1062, and the z axis 1064 (FIG. 6) during one of the heel strikesdescribed above in conjunction with FIGS. 9-12 while the patient 1070(FIGS. 7-8) is walking forward with a normal gait, according to anembodiment. In the described example, the x, y, and z axes have theideal alignment described in conjunction with FIGS. 7-8, the kneeprosthesis 1072 (FIGS. 7-8) exhibits instability but exhibits little orno wear-induced degradation, and the IMU 1022 samples each of the analogacceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) at the same sampletimes, the sampling rate (sometimes called the “raw sampling rate”) is3200 Hz, the ODR (effective sampling rate) is 800 Hz. Here,“instability” means that the bending of the knee prosthesis 1072 (FIGS.7-8) is not smooth while the patient 1070 is walking. That is, the kneeprosthesis 1072 exhibits instability if the femoral component of theknee prosthesis vibrates along, or about, one or more of the x, y, and zaxes 1060, 1062, and 1064 in an unintended, or in an otherwiseundesirable, manner.

FIG. 17 is a plot 1098, versus frequency, of the respective spectraldistributions X(f), Y(f), and Z(f) (in arbitrary units such as Joules,logarithmic scale) of the x, y, and z accelerations represented by thedigitized versions of the analog acceleration signals a_(x)(t),a_(y)(t), and a_(z)(t) of FIG. 16, according to an embodiment. Forexample, a server (e.g., a cloud server) remote from the knee prosthesiscan generate the spectral distributions X(f), Y(f), and Z(f) by takingthe Discrete Fourier Transform (DFT), or (Fast Fourier Transform (FFT)),of each of the digitized versions of the analog acceleration signalsa_(x)(t), a_(y)(t), and a_(z)(t).

FIG. 18 is a plot 1100, versus frequency, of the cumulative spectraldistribution XYZ(f) (in arbitrary units such as Joules Root Mean Square,logarithmic scale) of the x, y, and z accelerations represented by thedigitized versions of the analog acceleration signals a_(x)(t),a_(y)(t), and a_(z)(t) of FIG. 16, according to an embodiment. Forexample, a server (e.g., a cloud server) remote from the knee prosthesis1072 (FIGS. 7-8) can generate the cumulative spectral distribution byintegrating each of the distributions X(f), Y(f), and Z(f) (FIG. 14)over time and by summing together the respective integration results.

Referring to FIGS. 16-18, an analysis of the cumulative spectraldistribution XYZ(f) shows that for a knee prosthesis 1072 that exhibitsinstability but no degradation, approximately 90% of the RMS motion isat frequencies of less than 28 Hz (compared to 10 Hz (FIGS. 13-15) forthe knee prosthesis 1072 exhibiting no instability), and approximately98% of the RMS motion is at frequencies less than 44 Hz (compared to 20Hz (FIGS. 13-15) for the knee prosthesis 1072 exhibiting noinstability). The frequency ranges at 90% and 98% for the RMS motion ofthe knee prosthesis 1072 being significantly wider than thecorresponding benchmark frequency ranges for the RMS motion of the kneeprosthesis exhibiting no instability and no degradation can beindicative of the knee prosthesis 1072 exhibiting at least one ofinstability or degradation (early experimental results tend toward theRMS motion frequency ranges yielded by the spectral distribution XYZ(f)plotted in FIG. 18 being indicative of knee-prosthesis instability, andnot being indicative of degradation).

To determine the magnitude, type, and other characteristics of theinstability that the knee prosthesis 1072 (FIGS. 7-8) exhibits, one cananalyze (e.g., automatically on a server, such as a cloud server, remotefrom the knee prosthesis), for example, one or more of the followingparameters:

-   -   (1) the magnitudes, numbers, and relative phases of the peaks of        one or more of the digitized versions of the analog acceleration        signals a_(x)(t), a_(y)(t), and a_(z)(t) of FIG. 16;    -   (2) the respective magnitude of each of one or more of the        spectral distributions X(f), Y(f), and Z(f) at each of one or        more frequencies; and    -   (3) the respective magnitude of the cumulative spectral        distribution XYZ(f) at each of one or more frequencies.

As described elsewhere in this patent application, one can use one ormore deterministic algorithms, or one or more machine-learningalgorithms (e.g., neural networks), to characterize the instability andto suggest one or more procedures for remediating the instability. Forexample, an algorithm can process one or more of the digitized versionsof the analog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t)(FIG. 16), the spectral distributions X(f), Y(f), and Z(f), and thecumulative spectral distribution XYZ(f) to determine a peak-to-peakmagnitude (e.g., less than 2 millimeters (mm) translation or rotation,2-3 millimeters (mm) translation or rotation, and 3+ mm translation orrotation) of the instability, a likely cause (e.g., too much “slop”between the femoral component and the spacer (“puck”)) of theinstability, and a procedure (e.g., resize and replace the puck, sendthe patient 1070 (FIGS. 7-8) to physical therapy to tighten the muscles,ligaments, and tendons associated with the knee prosthesis) likely toremediate the instability.

Still referring to FIGS. 16-18, alternate embodiments of the describedanalyses and algorithms for detecting, quantifying, and proposingremediation of instability in the knee prosthesis 1072 (FIGS. 7-8) arecontemplated. For example, the described analyses and algorithms can beused, or can be modified for use, with an implantable prosthesis otherthan a knee prosthesis. Furthermore, embodiments described inconjunction with FIGS. 3-15 and 19-27 may be applicable to the analysesand algorithms described in conjunction with FIGS. 16-18.

FIG. 19 is a plot 1102, versus time, of the digitized versions of theanalog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) (in unitsof m/s²) that the accelerometers of the IMU 1022 (FIG. 4) respectivelygenerate in response to accelerations along the x axis 1060, the y axis1062, and the z axis 1064 (FIG. 6) during one of the heel strikesdescribed above in conjunction with FIGS. 9-12 while the patient 1070(FIGS. 7-8) is walking forward with a normal gait, according to anembodiment. In the described example, the x, y, and z axes have theideal alignment described in conjunction with FIGS. 7-8, the kneeprosthesis 1072 (FIGS. 7-8) exhibits instability and early-onsetwear-induced degradation, and the IMU 1022 samples each of the analogacceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) at the same sampletimes, the sampling rate is 3200 Hz, and the ODR is 800 Hz. Here,“early-onset degradation” means that the knee prosthesis 1072 (FIGS.7-8) has just begun to exhibit symptoms (e.g., rough engagement(grinding) of the femoral component with the plastic spacer of the kneeprosthesis) of wear induced by repeated flexing of the knee prosthesis.That is, the knee prosthesis 1072 exhibits wear if the femoral componentroughly engages, e.g., grinds against, the plastic spacer while thepatient 1070 (FIGS. 7-8) flexes the knee prosthesis, e.g., whilewalking.

FIG. 20 is a plot 1104, versus frequency, of the respective spectraldistributions X(f), Y(f), and Z(f) (in arbitrary units such as Joules,logarithmic scale) of the x, y, and z accelerations represented by thedigitized versions of the analog acceleration signals a_(x)(t),a_(y)(t), and a_(z)(t) of FIG. 19, according to an embodiment. Forexample, a server (e.g., a cloud server) remote from the knee prosthesiscan generate the spectral distributions X(f), Y(f), and Z(f) by takingthe Discrete Fourier Transform (DFT), or (Fast Fourier Transform (FFT)),of each of the digitized versions of the analog acceleration signalsa_(x)(t), a_(y)(t), and a_(z)(t).

FIG. 21 is a plot 1106, versus frequency, of the cumulative spectraldensity XYZ(f) (in arbitrary units such as Joules Root Mean Square,logarithmic scale) of the x, y, and z accelerations represented by thedigitized versions of the analog acceleration signals a_(x)(t),a_(y)(t), and a_(z)(t) of FIG. 19, according to an embodiment. Forexample, a server (e.g., a cloud server) remote from the knee prosthesis1072 (FIGS. 7-8) can generate the cumulative spectral density byintegrating each of the spectral distributions X(f), Y(f), and Z(f)(FIG. 14) over time and by summing together the respective integrationresults.

Referring to FIGS. 19-21, an analysis of the cumulative spectraldistribution XYZ(f) shows that for a knee prosthesis 1072 that exhibitsinstability and early-onset degradation, approximately 90% of the RMSmotion is at frequencies of less than 34 Hz (compared to 10 Hz (FIGS.13-15) for the knee prosthesis exhibiting no instability and nodegradation, and 28 HZ (FIGS. 16-18) for the knee prosthesis exhibitinginstability but no degradation), and approximately 98% of the RMS motionis at frequencies less than 175 Hz (compared to 20 Hz (FIGS. 13-15) forthe knee prosthesis 1072 exhibiting no instability and no degradation,and 44 HZ (FIGS. 16-18) for the knee prosthesis exhibiting instabilitybut no degradation). The frequency ranges at 90% and 98% for the RMSmotion of the knee prosthesis 1072 being significantly wider than thecorresponding benchmark frequency ranges for the RMS motion of the kneeprosthesis exhibiting no instability and no degradation and thecorresponding frequency ranges for the RMS motion of the knee prosthesisexhibiting instability but no degradation, can be indicative of the kneeprosthesis 1072 exhibiting both instability and early-onset degradation.

To determine the magnitude, type, and other characteristics of theinstability and the degradation that the knee prosthesis 1072 (FIGS.7-8) exhibits, one can analyze (e.g., automatically on a server, such asa cloud server, remote from the knee prosthesis), for example, one ormore of the following parameters:

-   -   (1) the magnitudes, numbers, and relative phases of the peaks of        one or more of the digitized versions of the analog acceleration        signals a_(x)(t), a_(y)(t), and a_(z)(t) of FIG. 19;    -   (2) the respective magnitude of each of one or more of the        spectral distributions X(f), Y(f), and Z(f) of FIG. 20 at each        of one or more frequencies; and    -   (3) the respective magnitude of the cumulative spectral        distribution XYZ(f) of FIG. 21 at each of one or more        frequencies.

As described elsewhere in this patent application, one can use one ormore deterministic algorithms, or one or more machine-learningalgorithms (e.g., neural networks), to characterize one or both of theinstability and the degradation and to suggest one or more proceduresfor remediating one or both of the instability and the degradation. Forexample, an algorithm can process one or more of the digitized versionsof the analog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t)(FIG. 16), the spectral distributions X(f), Y(f), and Z(f), and thecumulative spectral distribution XYZ(f) to determine a peak-to-peakmagnitudes (e.g., less than 2 millimeters (mm) translation or rotation,2-3 millimeters (mm) translation or rotation, and 3+ mm translation orrotation) of one or both of the instability and the degradation, likelycauses (e.g., too much “slop” between the femoral component and thespacer (“puck”) for instability, wear of the puck or femoral componentfor degradation) of one or both of the instability and the degradation,and procedure (e.g., resize and replace the puck, send the patient 1070(FIGS. 7-8) to physical therapy to tighten the muscles, ligaments, andtendons associated with the knee prosthesis) likely to remediate one orboth of the instability and the degradation.

Still referring to FIGS. 19-21, alternate embodiments of the describedanalyses and algorithms for detecting, quantifying, and proposingremediation of instability in the knee prosthesis 1072 (FIGS. 7-8) arecontemplated. For example, the described analyses and algorithms can beused, or can be modified for use, with an implantable prosthesis otherthan a knee prosthesis. Furthermore, embodiments described inconjunction with FIGS. 3-18 and 22-27 may be applicable to the analysesand algorithms described in conjunction with FIGS. 19-21.

FIG. 22 is a plot 1108, versus time, of the digitized versions of theanalog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) (in unitsof m/s²) that the accelerometers of the IMU 1022 (FIG. 4) respectivelygenerate in response to accelerations along the x axis 1060, the y axis1062, and the z axis 1064 (FIG. 6) during one of the heel strikesdescribed above in conjunction with FIGS. 9-12 while the patient 1070(FIGS. 7-8) is walking forward with a normal gait, according to anembodiment. In the described example, the x, y, and z axes have theideal alignment described in conjunction with FIGS. 7-8, the kneeprosthesis 1072 (FIGS. 7-8) exhibits instability and advancedwear-induced degradation, the IMU 1022 samples each of the analogacceleration signals a_(x)(t), a_(y)(t), and a_(z)(t) at the same sampletimes, the sampling rate is 3200 Hz, and the ODR is 800 Hz. Here,“advanced degradation” means that the knee prosthesis 1072 (FIGS. 7-8)apparently has been exhibiting symptoms (e.g., rough engagement(grinding) of the femoral component with the plastic spacer of the kneeprosthesis) of wear induced by repeated flexing of the knee prosthesis.That is, the knee prosthesis 1072 exhibits wear if the femoral componentroughly engages, e.g., grinds against, the plastic spacer while thepatient 1070 (FIGS. 7-8) flexes the knee prosthesis, e.g., whilewalking.

FIG. 23 is a plot 1110, versus frequency, of the respective spectraldistributions X(f), Y(f), and Z(f) (in arbitrary units such as Joules,logarithmic scale) of the x, y, and z accelerations represented by thedigitized versions of the analog acceleration signals a_(x)(t),a_(y)(t), and a_(z)(t) of FIG. 22, according to an embodiment. Forexample, a server (e.g., a cloud server) remote from the knee prosthesiscan generate the spectral distributions X(f), Y(f), and Z(f) by takingthe Discrete Fourier Transform (DFT), or (Fast Fourier Transform (FFT)),of each of the digitized versions of the analog acceleration signalsa_(x)(t), a_(y)(t), and a_(z)(t).

FIG. 24 is a plot 1112, versus frequency, of the cumulative spectraldistribution XYZ(f) (in arbitrary units such as Joules Root Mean Square,logarithmic scale) of the x, y, and z accelerations represented by thedigitized versions of the analog acceleration signals a_(x)(t),a_(y)(t), and a_(z)(t) of FIG. 22, according to an embodiment. Forexample, a server (e.g., a cloud server) remote from the knee prosthesis1072 (FIGS. 7-8) can generate the cumulative spectral distribution byintegrating each of the contents X(f), Y(f), and Z(f) (FIG. 14) overtime and by summing together the respective integration results.

Referring to FIGS. 22-24, an analysis of the cumulative spectraldistribution XYZ(f) shows that for a knee prosthesis 1072 that exhibitsinstability and early-onset degradation, approximately 90% of the RMSmotion is at frequencies less than 306 Hz (compared to 10 Hz (FIGS.13-15) for the knee prosthesis exhibiting no instability and nodegradation, 28 Hz (FIGS. 16-18) for the knee prosthesis exhibitinginstability but no degradation, and 34 Hz for the knee prosthesisexhibiting instability and early-onset degradation), and approximately98% of the RMS motion is at frequencies less than 394 Hz (compared to 20Hz (FIGS. 13-15) for the knee prosthesis 1072 exhibiting no instabilityand no degradation, 44 HZ (FIGS. 16-18) for the knee prosthesisexhibiting instability but no degradation, and 175 Hz for the kneeprosthesis exhibiting instability and early-onset degradation). Thefrequency ranges at 90% and 98% for the RMS motion of the kneeprosthesis 1072 being significantly wider than the correspondingbenchmark frequency ranges for the RMS motion of the knee prosthesisexhibiting no instability and no degradation, and the correspondingfrequency ranges for the RMS motion of the knee prosthesis exhibitinginstability but no degradation and instability and early-onsetdegradation, can be indicative of the knee prosthesis 1072 exhibitingboth instability and advanced degradation.

To determine the magnitude, type, and other characteristics of theinstability and the degradation that the knee prosthesis 1072 (FIGS.7-8) exhibits, one can analyze (e.g., automatically on a server, such asa cloud server, remote from the knee prosthesis), for example, one ormore of the following parameters:

-   -   (1) the magnitudes, numbers, and relative phases of the peaks of        one or more of the digitized versions of the analog acceleration        signals a_(x)(t), a_(y)(t), and a_(z)(t) of FIG. 22;    -   (2) the respective magnitude of each of one or more of the        spectral distributions X(f), Y(f), and Z(f) of FIG. 23 at each        of one or more frequencies; and    -   (3) the respective magnitude of the cumulative spectral        distribution XYZ(f) of FIG. 24 at each of one or more        frequencies.

As described elsewhere in this patent application, one can use one ormore deterministic algorithms, or one or more machine-learningalgorithms (e.g., neural networks), to characterize one or both of theinstability and the degradation and to suggest one or more proceduresfor remediating one or both of the instability and the degradation. Forexample, an algorithm can process one or more of the digitized versionsof the analog acceleration signals a_(x)(t), a_(y)(t), and a_(z)(t)(FIG. 22), the spectral distributions X(f), Y(f), and Z(f), and thecumulative spectral distribution XYZ(f) to determine a peak-to-peakmagnitudes (e.g., less than 2 millimeters (mm) translation or rotation,2-3 millimeters (mm) translation or rotation, and 3+ mm translation orrotation) of one or both of the instability and the degradation, likelycauses (e.g., too much “slop” between the femoral component and thespacer (“puck”) for instability, wear of the puck or femoral componentfor degradation) of one or both of the instability and the degradation,and procedure(s) (e.g., resize and replace the puck, send the patient1070 (FIGS. 7-8) to physical therapy to tighten the muscles, ligaments,and tendons associated with the knee prosthesis) likely to remediate oneor both of the instability and the degradation.

Still referring to FIGS. 22-24, alternate embodiments of the describedanalyses and algorithms for detecting, quantifying, and proposingremediation of instability in the knee prosthesis 1072 (FIGS. 7-8) arecontemplated. For example, the described analyses and algorithms can beused, or can be modified for use, with an implantable prosthesis otherthan a knee prosthesis. Furthermore, an algorithm can generate resultsin response to the digitized versions of one of more of the angularvelocities Ω_(x)(t), Ω_(y)(t), and Ω_(z)(t) (in units of degrees/s) thatthe gyroscopes of the IMU 1022 (FIG. 4) respectively generate inresponse to angular velocities about the x axis 1060, the y axis 1062,and the z axis 1064 (FIG. 6). Moreover, an algorithm can generateresults in response to one or more portions (e.g., toe off) of the gaitof the patient 1070 (FIGS. 7-8) other than, or in addition too, a heelstrike. In addition, embodiments described in conjunction with FIGS.3-21 and 25-27 may be applicable to the analyses and algorithmsdescribed in conjunction with FIGS. 22-24.

FIG. 25 is a flow diagram 1120 of the operation of the implantablecircuit 1010 of FIG. 4, according to an embodiment.

Referring to FIGS. 4 and 25, at a step 1122, the implantable circuit1010 detects movement of the implanted prosthesis, such as the kneeprosthesis 1072 of FIGS. 7-8. For example, the control circuit 1032monitors the respective digitized output signal from each of one or moreof the accelerometers and gyroscopes of the IMU 1022 and detectsmovement of the implanted prosthesis in response to a respectivemagnitude of each of one or more of the digitized output signalsexceeding a movement-detection threshold.

Next, at a step 1124, in response to detecting movement of the implantedprosthesis at step 1122, the control circuit 1032 causes the IMU 1022 tosample the analog signals output from one or both of the IMUaccelerometers and gyroscopes (it is assumed hereinafter that the IMU1022 samples the analog signals output from both of IMU accelerometersand gyroscopes). The IMU 1022 samples the analog signals at the samesampling rate, or at respective sampling rates. For example, the IMU1022 samples the analog signals output from all of the x, y, and zaccelerometers and gyroscopes at 1600 Hz (raw sampling rate), and scalesdown the raw sampling rate to achieve, for each of the accelerometer andgyroscope signals, an effective sampling rate (also called the outputdata rate (ODR)) of 800 Hz. Furthermore, the control circuit 1032 causesthe IMU 1022 to sample the analog signals output from the accelerometersand gyroscope for a finite time, such as, for example, during a timewindow of ten seconds.

Then, at a step 1126, the control circuit 1032 determines whether thesamples that the IMU 1022 took at step 1124 are samples of a qualifiedevent, such as the patient 1070 (FIGS. 7-8) walking with the implantedknee prosthesis 1072 (FIGS. 7-8). For example, the control circuit 1032correlates the respective samples from each of one or more of theaccelerometers and gyroscopes with corresponding benchmark samples(e.g., stored in memory circuit 1024 of FIG. 4) of the qualified event,compares the correlation result to a threshold, and determines that thesamples are of a qualified event if the correlation result equals orexceeds the threshold or determines that the samples are not of aqualified event if the correlation result is less than the threshold.Alternatively, the control circuit 1032 may perform a less-complex, andless energy-consuming determination by determining that the samples areof a qualified event if, for example, the samples have a peak-to-peakamplitude and a duration that could indicate that the patient is walkingfor a threshold length of time. A determination as to whether thesamples actually were taken while the patient was walking can be made bythe remote destination (e.g., a cloud server). For example, if thecontrol circuit 1032 is configured to cause the IMU 1022 to sample, at arelatively high sample rate (e.g., 3200 Hz) and ODR (e.g., 800 Hz), theanalog signals output by one or both of the accelerometers andgyroscopes in response to three detected patient movements per day, and,statistically, at least one of the detected movements is the patientwalking for at least a threshold length of time, then this technique canprovide suitable prosthesis information while consuming less energy fromthe battery 1012 than the IMU would consume sampling fewer events butdetermining that the movement corresponding to the sampled events is thepatient walking.

If the control circuit 1032 determines that the samples that the IMU1022 took at step 1124 are not of a qualified event, then the controlcircuit returns to step 1122.

But if the control circuit 1032 determines that the samples that the IMU1022 took at step 1124 are of a qualified event, then the controlcircuit proceeds to a step 1128, during which the control circuitstores, for each set of samples, in the memory circuit 1024, the samplesthemselves and respective sample information. A set of samples includesthe samples from a respective one of the accelerometers and gyroscopes,and the sample information includes, for example, the identity of theaccelerometer or gyroscope that generated the analog signal of which thesamples of the set were taken, the raw sample rate and the ODR, thestart time of the sample set (the time at which the first sample of theset was taken), the end time of the sample set (the time at which thelast sample of the set was taken), the length of the sample window, andthe dynamic amplitude input range and the amplitude output range of theADC that took the samples. The dynamic amplitude input range is themaximum peak, or peak-to-peak, signal amplitude that the ADC can acceptwithout “cutting off” the input signal. And the amplitude output rangeis the maximum peak, or peak-to-peak, range that the samples cover, andis an indication of the analog amplitude represented by each digitalsample. If the sample information (e.g., raw sample rate, ODR, samplewindow) is the same for the respective samples from each accelerometerand gyroscope, then the control circuit 1032 can group all of theaccelerometer and gyroscope samples taken during a same time window intoa single set of samples with common sample information.

Next, at a step 1130, the control circuit 1032 generates, for eachstored set of samples and corresponding sample information, a respectivemessage including a header and a payload. The header includes the sampleinformation, and the payload includes the samples that form the set. Theheader may also include additional information, such as a uniqueidentifier (e.g., a serial number) of the implanted prosthesis, a uniqueidentifier of the patient 1070 (FIGS. 7-8), and the length of thepayload.

Then, at step 1132, which is optional, the control circuit 1032 encryptspart or all of each message, for example, as may be specified by HIPAA.As part of this step or step 1130, the control circuit 1032 may include,in the message header, a public encryption key that allows an authorizedrecipient of the message to decrypt the encrypted portion of themessage. Alternatively, the control circuit 1032 may not encrypt thesample messages or may not perform any encryption until transmitting thesample messages to a remote destination.

Next, at a step 1134, the control circuit 1032 stores each message,encrypted or not, in the memory 1024.

Then, at a step 1136, the control circuit 1032 determines whether thebase station 1004 (FIG. 3) has polled the implantable circuit 1010 forall messages generated since the last time that the implantable circuitsent messages to the base station.

If the control circuit 1032 determines that the base station 1004 (FIG.3) has not polled the implantable circuit 1010, then the control circuittakes no further action regarding the messages, and effectively waitsfor the base station to poll the implantable circuit.

But if the control circuit 1032 determines that the base station 1004(FIG. 3) has polled the implantable circuit 1010, then the controlcircuit proceeds to a step 1138.

At the step 1138, the control circuit 1032 generates one or more datapackets that collectively include the messages stored in the memory 1024as described above in conjunction with the step 1134. The controlcircuit 1032 generates the one or more data packets according to anysuitable communication protocol, and each of the data packets includes aheader and a payload. The header includes information such as anidentifier (e.g., serial number) unique to the implanted prosthesis, anidentifier unique to the patient 1070 (FIGS. 7-8), and a sequence numberthat represents a relative position within the sequence of data packetsthat the control circuit 1032 will send to the base station 1004 (FIG.4) (information in the data-packet header may be redundant relative tosome or all of the information in the message header). And the payloadincludes one or more of the messages (in whole or in part) stored in thememory circuit 1024. For example, if a stored message is too long for asingle data packet, then the control circuit 1032 can split the messageinto two or more data packets (hence the sequence number allows adestination of the message to reconstruct the message). In contrast, ifa stored message is not long enough to fill the payload of the datapacket, then the data packet may include the message plus one or moreother messages in whole or in part. Furthermore, instead of includingthe message header, the data-packet payload can include only the messagepayload (the samples), and the contents of the message header can bemerged with, or otherwise included in, the data-packet header.

Next, at a step 1140, which is optional, the control circuit 1032encrypts part or all of each data packet, for example, at least theprosthesis and patient identifiers as may be specified by HIPAA. As partof this step or step 1140, the control circuit 1032 may include, in thedata-packet header, a public encryption key that allows an authorizedrecipient of the message to decrypt the encrypted portion of thedata-packet header. If some or all of the message is encrypted per step1132, then the control circuit 1032 may decrypt the message beforeforming the data packet. Alternatively, the control circuit 1032 maymaintain the message in encrypted form such that at least a portion ofthe encrypted portion of the message is double encrypted (message-levelencryption and data-packet-level encryption). In another alternative,the control circuit 1032 may not encrypt the prosthesis identifier sothat the base station 1004 or smart device 1005 (FIG. 3) can use theprosthesis identifier to determine whether the base station or smartdevice should ignore the data packet or receive and process the datapacket.

Then, at a step 1142, the control circuit 1032 error encodes the one ormore data packets, whether encrypted or unencrypted, according to anysuitable error-encoding technique (the communication protocol with whichthe one or more data packets are compatible may specify theerror-encoding technique). Error encoding the one or more data packetsallows the destination to recover a data packet having an error acquiredduring propagation of the data packet from the control circuit 1032 tothe destination.

Next, at a step 1144, the control circuit 1032 transmits theerror-encoded one or more data packets to the base station 1004 (FIG. 3)via the RF transceiver 1025, filter 1028, and antenna 1030.Alternatively, the control circuit 1032 transmits the error-encoded oneor data packets to the base station 1004 via the smart device 1005, tothe smart device 1005 directly, or to the smart device via the basestation.

Then, at a step 1146, the control circuit 1032 determines whether it istime to acquire samples of another qualified event.

If the control circuit 1032 determines that it is not yet time toacquire samples of another qualified event, then the control circuitcauses the implantable circuit 1010 to enter a sleep, or otherlow-power, mode, at a step 1148 to save power and extend the life of thebattery 1012. For example, the control circuit 1032 may open theswitches 1016 and 1018 to cut power to the IMU 2022 and the memorycircuit 1024, respectively. Furthermore, the clock-and-power-managementcircuit 1020 includes a timer that notifies the control circuit 1032 to“wake up” the implantable circuit 1010 at a programmed absolute time orafter a programmed amount of time (e.g., one day, two days, one week,one month) has elapsed. In addition, the time between qualified eventscan be related e.g., to how long it has been since the prosthesis wasimplanted in the patient 1070 (FIGS. 7-8) post-implantation or tohealth-insurance billing codes such as telemedicine codes or CPT codes.Regarding the former, for example, for the first three months (0-3months) post implant, the control circuit 1032 is configured to measureat least one qualified event (e.g., a walking of at least ten steps)each day so that the patient's physician can monitor the functioning ofthe implant. Then, 3-6 months post implant, the control circuit 1032 maybe configured to measure at least one qualified event every other day,e.g., after a waiting period of at least 24 hours, or every third day,e.g., after a waiting period of at least 48 hours. From 6-12 months thecontrol circuit 1032 may be configured to measure at least one qualifiedevent per week, and thereafter one or two qualified events per month.Regarding the latter (health-insurance billing codes), a telemedicinecode or CPT code is an insurance code under which a physician can billan insurance company for reviewing patient information remotely, such asover the internet, by email, or by phone. An example of such informationis the result of an analysis performed on the samples of one or morequalified events detected and sampled by the IMU 1022 (FIG. 4). Theinsurance plan typically specifies the maximum payment that thephysician can receive (e.g., $3000/year) under telemedicine codes or CPTcodes for a medical issue (e.g., knee prosthesis), and how frequentlythe physician must the review patient information to qualify for themaximum payment. Consequently, the control circuit 1032 or otherportions (e.g., the Clock and Power Management circuit 1020) of theimplantable circuit 1010 can be configured to detect and measure aqualified prosthesis event at a frequency that allows a patient'sphysician to qualify for the payment that he/she can receive from aninsurance company under one or more telemedicine, CPT or otherreimbursement codes. For example, if an insurance plan requires aphysician to review the results yielded by analyzing samples generatedby the IMU 1022 daily for 0-6 months post implant, weekly for 6-12months post implant, and monthly thereafter, then one can configure thecontrol circuit 1032, or other portions of the implantable circuit 1010,to detect, to sample, and to store samples of at least one qualifiedevent per day for months 0-6, of at least one qualified event per weekfor months 6-12, and at least one qualified event per month thereafter.Alternatively, one can configure the control circuit 1032, or otherportions of the implantable circuit 1010, to detect, to sample, and tostore samples of at least one qualified event per day for at leastsixteen days per month.

If, however, the control circuit 1032 determines, at the step 1146, thatit is time to acquire samples of another qualified event, then thecontrol circuit returns to step 1122.

Still referring to FIG. 25, alternate embodiments of the operation ofthe implantable circuit 1010 are contemplated. For example, one or moreof the steps of the flow diagram 1120 may be omitted, and one or moreadditional steps may be added. In addition, embodiments described inconjunction with FIGS. 3-24 and 26-27 may be applicable to the operationof the implantable circuit 1010.

FIG. 26 is a flow diagram 1160 of the operation of the base-stationcircuit 1040 of FIG. 5, according to an embodiment.

Referring to FIGS. 5 and 26, at a step 1162, the base-station circuit1040 polls the implantable circuit 1010 (FIG. 4) for data packets thatinclude kinematic-movement messages (if any) that the implantablecircuit has generated since the last time that the implantable circuitsent data packets to the base station 1004 (FIG. 3).

Next, at a step 1164, the base-station circuit 1040 determines whetherit has received, from the implantable circuit 1010 (FIG. 4) of theimplanted prosthesis (e.g., the knee prosthesis 1072 of FIGS. 7-8), avalid response to the poll. For example, the base-station circuit 1040determines whether it has received a valid response from the implantedprosthesis by comparing the implant identifier in the response to aversion of the implant identifier stored in the base station's memorycircuit 1056 to determine whether the implant is registered to the basestation 1004. If the implant identifier in the response is encrypted,then the base-station circuit 1040 decrypts the response beforedetermining whether the implant identifier is valid registered to thebase station 1004.

If the base-station circuit 1040 determines that it has not yet receiveda valid polling response from the implantable circuit 1010 (FIG. 4),then, at a step 1166, the control circuit 1058 determines whether anumber of unsuccessful polling attempts during the present pollingperiod exceeds a first threshold, Threshold_1.

If, at the step 1166, the control circuit 1058 determines that thenumber of unsuccessful polling attempts does not exceed Threshold_1,then the control circuit returns to the step 1162 and again polls theimplantable circuit 1010 of the implanted prosthesis; the controlcircuit may wait a programmed delay time before re-polling theimplantable circuit. For example, Threshold_1 may have a value in anapproximate range of 1-100.

But if, at the step 1166, the control circuit 1058 determines that thenumber of unsuccessful polling attempts exceeds Threshold_1, then thecontrol circuit proceeds to a step 1168.

At the step 1168, the control circuit 1058 transmits, via the RFtransceiver 1054, filter 1050, and antenna 1046, an error message to adestination, such as cloud or other server, where the error messageindicates that the implanted prosthesis is not responding tobase-station polling. As described elsewhere in this application, thedestination may take appropriate action, such as notifying the patient1070 (FIGS. 7-8) via email or text to check that the base station 1004(FIG. 3) is powered “on” and is properly linked to the patient's homenetwork 1006 (FIG. 3).

Referring again to the step 1164, if the control circuit 1058 determinesthat it has received a valid response to its poll of the implantablecircuit 1010 of FIG. 4, then the control circuit proceeds to a step1170.

At the step 1170, the control circuit 1058 receives, from theimplantable circuit 1010 (FIG. 4) of the implantable prosthesis via theantenna 1044, filter 1048, and RF transceiver 1052, data packets thatinclude the samples taken by the IMU 1022 (FIG. 4) and relatedinformation. The control circuit 1058 also decodes and decrypts (ifneeded) the data packets and parses the IMU samples and relatedinformation (e.g., unique prosthesis identifier, unique patientidentifier).

At a step 1172, the control circuit 1058 determines whether the patientand prosthesis identifiers, and the data, parsed from the received datapackets per the step 1170 are correct (if the control circuit 1058already determined that the prosthesis identifier is correct per step1164, then the control circuit may forgo again determining whether theprosthesis identifier is correct). For example, the control circuit 1058error decodes a data packet using a suitable error-decoding algorithm(e.g., cyclic-redundancy check (CRC), Reed-Solomon) that corresponds tothe error-encoding algorithm used by the control circuit 1032 (FIG. 4)and determines whether the data packet includes an unrecoverable errorin response to the decoding result. Ad if the control circuit 1058determines that the data packet includes no unrecoverable error, thenthe control circuit 1058 compares the received patient and prosthesisidentifiers with respective identifiers stored in the memory circuit1056 or downloaded from remote location. If the control circuit 1058determines that the data packet includes an unrecoverable error or thatat least one of the received patient and prosthesis identifiers isincorrect, then the control circuit proceeds to a step 1174; otherwise,the control circuit 1058 acknowledges (e.g., according to a suitablehandshake protocol), to the implant circuit 1010, receipt of a validdata packet, and proceeds to a step 1176.

At the step 1174, the base-station control circuit 1058 determineswhether the number of times that it has received an erroneous datapacket (e.g., a data packet with an unrecoverable error or an incorrectpatient identifier or an incorrect prosthesis identifier) during thecurrent polling cycle exceeds a second threshold Threshold_2. If thecontrol circuit 1058 determines that the number of times an erroneousdata packet has been received during the current polling cycle does notexceed Threshold_2, then the control circuit returns to the step 1162and repolls the implanted circuit 1010 (FIG. 4) of the prosthesis toresend the data packet that the control circuit 1058 determined to beerroneous upon receipt at the base station 1004 (FIG. 3). But if thecontrol circuit 1058 determines that the number of times an erroneousdata packet has been received during the current polling cycle doesexceed Threshold_2, then the control circuit proceeds to the step 1168and sends an error message as described above.

If, at the step 1172, the base-station control circuit 1058 determinesthat the patient and prosthesis identifiers are correct, then, at thestep 176, the control circuit generates base-station data packets thatinclude the parsed messages from the implantable circuit 1010 (FIG. 4)of the implanted prosthesis and that conform to any suitablecommunication protocol. That is, the control circuit 1058 effectivelyre-packetizes the messages into one or more base-station data packets.The respective header of each base-station data packet may include someor all of the information in the message headers and prosthesis datapackets received from the prosthesis, plus additional information suchas base-station-data-packet-routing information, e.g., internet, orother, addresses of the packet source (e.g., home network 1006 (FIG. 3))and packet destination (e.g., cloud server). And the respective payloadof each data packet includes accelerometer or gyroscope samples taken bythe IMU 1022 (FIG. 4). Furthermore, if a message is too long for asingle base-station data packet, then the control circuit 1058 can splitthe message into two or more data packets (hence the sequence numberallows a destination of the message to reconstruct the message). Incontrast, if a message is not long enough to fill the payload of thedata packet, then the data packet may include the message plus one ormore other messages in whole or in part. Moreover, instead of includingthe message header, the data-packet payload can include only the messagepayload (the samples), and the contents of the message header can bemerged with, or otherwise included in, the data-packet header.

Then, at step 1178, the base-station control circuit 1058 encrypts partor all of each base-station data packet, for example, as may bespecified by one or both of HIPAA and the communication protocol viawhich the control circuit sends base-station data packets. As part ofthis step or step 1176, the control circuit 1058 may include, in thedata-packet header, a public encryption key that allows an authorizedrecipient of the data packet to decrypt the encrypted portion of thedata packet. If some or all of the message or prosthesis data packet isencrypted, then the control circuit 1058 may decrypt the message beforeforming the base-station data packet. Alternatively, the control circuit1058 may maintain the message and prosthesis data packet in encryptedform such that at least a portion of the encrypted portion of thebase-station data packet is double or triple encrypted (two or more ofmessage-level encryption, prosthesis-data-packet-level encryption, andbase-station-data-packet-level encryption).

Then, at a step 1180, the control circuit 1032 error encodes the one ormore encrypted base-station data packets according to any suitableerror-encoding technique (the communication protocol with which the oneor more base-station data packets are compatible may specify theerror-encoding technique). Error encoding the one or more base-stationdata packets allows the destination to recover a data packet having anerror acquired during propagation of the data packet from thebase-station control circuit 1058 (FIG. 5) to the destination.

Next, at a step 1182, the base-station control circuit 1058 transmitsthe error-encoded one or more base-station data packets to thedestination (e.g., a cloud server) via the RF transceiver 1054, filter1050, antenna 1046, home network 1006 (FIG. 3), and the internet orother communications network.

Then, the base-station control circuit 1058 returns to the step 1162,waits a programmed time (e.g., one day, between two and six days, oneweek, one month), and then polls the implanted prosthesis again afterthe elapse of the programmed time.

Still referring to FIG. 26, alternate embodiments of the operation ofthe base-station circuit 1040 are contemplated. For example, the smartdevice 1005 (FIG. 3) may operate in a manner similar to that describedabove in conjunction with the flow diagram 1160. Furthermore, the smartbase-station circuit 1040 may perform one or more of the steps in theflow diagram 1160, and the smart device 1005 may perform the one or moreremaining steps in the flow diagram 1160. Moreover, as described above,the base-station circuit 1040 may communicate with the implantablecircuit 1010 (FIG. 4) via the smart device 1005, or the smart device maycommunicate with the implantable circuit via the base-station circuit.In addition, one or more of the steps of the flow diagram 1160 may beomitted, and one or more additional steps may be added. Furthermore,embodiments described in conjunction with FIGS. 3-25 and 27 may beapplicable to the operation of the base-station circuit 1040.

FIG. 27 is a flow diagram 1190 of the operation of the fuse 1014 and thecontrol circuit 1032 of FIG. 4, according to an embodiment.

Referring to FIGS. 4 and 27, at a step 1192, the fuse 1014 iselectrically closed and the control circuit 1032 determines whether acurrent from the battery 1012 through the fuse exceeds a firstovercurrent threshold. For example, the control circuit 1032, or anotherportion of the implantable circuit 1010, makes this determination bycomparing the current through the fuse 1014 with a reference thatrepresents the overcurrent threshold.

If the control circuit 1032 determines that the current through the fuse1014 exceeds the overcurrent threshold, then the control circuitproceeds to a step 1194; otherwise, the control circuit proceeds to astep 1196.

At the step 1194, the control circuit 1032 electrically opens the fuse1014, increments a count value, and implements a delay beforedetermining whether to re-close the fuse. To have the ability to openand re-close the fuse 1014, a connection between the battery 1012 andthe control circuit 1032 bypasses the fuse such that opening the fusedoes not cut power to the control circuit, or the control circuit has,or is coupled to, another power source (e.g., battery) that powers thecontrol circuit even while the fuse 1014 is open.

At the step 1196, the control circuit 1032 determines whether a currentfrom the battery 1012 through the fuse 1014 exceeds a second overcurrentthreshold for a first threshold length of time, where the secondovercurrent threshold is less than the first overcurrent threshold. Forexample, the control circuit 1032, or another portion of the implantablecircuit 1010, makes this determination by comparing the current throughthe fuse 1014 with a reference that represents the second overcurrentthreshold and by determining a length of time that the current isgreater than the second overcurrent threshold.

If the control circuit 1032 determines that the current through the fuse1014 exceeds the second overcurrent threshold for the first thresholdlength of time, then the control circuit proceeds to the step 1194 andopens the fuse, at least temporarily, as described above; otherwise, thecontrol circuit proceeds to a step 1198.

At a step 1198, the fuse 1014 the control circuit 1032 determineswhether a voltage across the closed fuse exceeds a first overvoltagethreshold. For example, the control circuit 1032, or another portion ofthe implantable circuit 1010, makes this determination by comparing thevoltage across the fuse 1014 with a reference that represents theovervoltage threshold.

If the control circuit 1032 determines that the voltage across the fuse1014 exceeds the overvoltage threshold, then the control circuitproceeds to the step 1194; otherwise, the control circuit proceeds to astep 1200.

At the step 1194, the control circuit 1032 electrically opens the fuse1014, at least temporarily, as described above.

At the step 1200, the control circuit 1032 determines whether thevoltage across the fuse 1014 exceeds a second overvoltage threshold fora second threshold length of time, where the second overvoltagethreshold is less than the first overvoltage threshold. For example, thecontrol circuit 1032, or another portion of the implantable circuit1010, makes this determination by comparing the voltage across the fuse1014 with a reference that represents the second overvoltage thresholdand by determining a length of time that the voltage is greater than thesecond overvoltage threshold.

If the control circuit 1032 determines that the voltage across the fuse1014 exceeds the second overvoltage threshold for the second thresholdlength of time, then the control circuit proceeds to the step 1194 andopens the fuse, at least temporarily, as described above; otherwise, thecontrol circuit proceeds to a step 1202.

At the step 1202, the control circuit 1032 determines whether atemperature of the closed fuse 1014 (or the temperature of another partof the prosthesis) exceeds a first overtemperature threshold. Forexample, the control circuit 1032, or another portion of the implantablecircuit 1010, makes this determination by comparing the temperature witha reference that represents the overtemperature threshold.

If the control circuit 1032 determines that the temperature exceeds theovertemperature threshold, then the control circuit proceeds to the step1194; otherwise, the control circuit proceeds to a step 1204.

At the step 1194, the control circuit 1032 electrically opens the fuse1014, at least temporarily, as described above.

At the step 1204, the control circuit 1032 determines whether thetemperature of the fuse 1014 (or the temperature of another part of theprosthesis) exceeds a second overtemperature threshold for a thirdthreshold length of time, where the second overtemperature threshold isless than the first overtemperature threshold. For example, the controlcircuit 1032, or another portion of the implantable circuit 1010, makesthis determination by comparing the temperature with a reference signalthat represents the second overtemperature threshold and by determininga length of time that the temperature is greater than the secondovertemperature threshold.

If the control circuit 1032 determines that the temperature exceeds thesecond overtemperature threshold for the third threshold length of time,then the control circuit proceeds to the step 1194 and opens the fuse,at least temporarily, as described above; otherwise, the control circuitproceeds to a step 1206.

At the step 1206, the control circuit 1032 maintains the fuse 1014electrically closed and returns to the step 1192.

If, however, the control circuitry 1032 proceeded to the step 1194 fromany of the steps 1192-1204, then the control circuit proceeds to a step1208.

At the step 1208, the control circuit 1032 determines whether the countexceeds a count threshold (the count represents the number of times thatthe control circuit has opened the fuse 1014 since the battery 1012 hasbeen powering the implantable circuit 1010. If the control circuit 1032determines that the count exceeds the count threshold, then the controlcircuit proceeds to a step 1210; otherwise, the control circuit proceedsto a step 1212.

At the step 1210, the control circuit 1032 opens the fuse 1014permanently. And if the prosthesis has a power source other than thebattery 1012 for powering the implantable circuit 1010 even while thefuse 1014 is open, then the control circuit 1032 generates an errormessage and one or more data packets that include the error message,stores the one or more data packets in the memory circuit 1024, andtransmits, via the RF transceiver 1026, the filter 1028, and the antenna1030, the one or more data packets to the base station 1004 (FIG. 3) inresponse to the next polling request from the base station.

At the step 1212, the control circuit 1032 determines if the delay haselapsed. If the delay has not elapsed, then the control circuit 1032effectively waits until the delay has elapsed. If, however, the delayhas elapsed, the control circuit 1032 proceeds to a step 1214.

At the step 1214, the control circuit 1032 closes the fuses 1014, andreturns to the step 1192. The steps 1212 and 1214 allow the controlcircuit 1032 to reset the fuse 1014 on the chance that the event thatcaused the control circuit 1032 to open the fuse at the step 1194 wastemporary such that the fuse need not be permanently opened.

Still referring to FIGS. 4 and 27, alternate embodiments of the fuse1014 and the related operation of the implantable circuit 1010 arecontemplated. For example, the fuse 1014 can be a one-time openable fusethat is not controllable by the control circuit 1032 such that once thefuse opens, it remains open. Furthermore, the fuse 1014 may open inresponse to fewer than all, or to only one, of the conditions describedin conjunction with steps 1192-1204. For example, the fuse 1014 may openonly in response to a current through the fuse exceeding an overcurrentthreshold per step 1192. Moreover, one or more of the steps of the flowdiagram 1190 may be omitted, and one or more additional steps may beadded. In addition, embodiments described in conjunction with FIGS. 3-26may be applicable to the fuse 1014 and the related operation of theimplantable circuit 1010.

The following are exemplary embodiments of the present disclosure:

1) An implantable medical device, comprising:

-   -   a. a circuit configured to be fixedly attached to an implantable        prosthetic device;    -   b. a power component; and    -   c. a device configured to uncouple the circuit from the power        component.

2) The implantable medical device of embodiment 1, wherein the circuitincludes an implantable reporting processor.

3) The implantable medical device of embodiment 1, wherein the powercomponent includes a battery.

4) The implantable medical device of embodiment 1, wherein the deviceincludes a fuse.

5) The implantable medical device of embodiment 1, wherein the deviceincludes a resettable fuse.

6) The implantable medical device of embodiment 1, wherein the deviceincludes a switch.

7) The implantable medical device of embodiment 1, wherein the deviceincludes a one-time openable fuse.

8) The implantable medical device of embodiment 1, wherein the device isconfigured to uncouple the circuit from the power component in responseto a current through the device exceeding a threshold current.

9) The implantable medical device of embodiment 1, wherein the device isconfigured to uncouple the circuit from the power component in responseto a voltage across the device exceeding a threshold voltage.

10) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature exceeding a threshold temperature.

11) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature of the circuit exceeding a thresholdtemperature.

12) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature of the power component exceeding a thresholdtemperature.

13) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature of the device exceeding a thresholdtemperature.

14) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a current through the device exceeding a threshold currentfor at least a threshold time.

15) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a voltage across the device exceeding a threshold voltagefor at least a threshold time.

16) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature exceeding a threshold temperature for at leasta threshold time.

17) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature of the circuit exceeding a thresholdtemperature for at least a threshold time.

18) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature of the power component exceeding a thresholdtemperature for at least a threshold time.

19) The implantable medical device of embodiment 1, wherein the deviceis configured to uncouple the circuit from the power component inresponse to a temperature of the device exceeding a thresholdtemperature.

20) The implantable medical device of embodiment 1, further comprisingat least one mechanical component that is configured to function whilethe device uncouples the circuit from the power component.

21) An implantable medical device, comprising:

-   -   a. a circuit configured to be fixedly attached to an implantable        prosthetic device;    -   b. a battery; and    -   c. a fuse coupled between the circuit and the battery.

22) A method, comprising electrically opening a fuse that is disposedbetween a circuit and a battery, at least the fuse and the circuit beingdisposed on an implanted prosthetic device.

23) The method of embodiment 22, further comprising operating at leastone mechanical component of the implanted prosthetic device while thefuse is electrically open.

24) The method of embodiment 22 wherein the battery is disposed on theimplanted prosthetic device.

25) An implantable medical device, comprising:

-   -   a. at least one sensor configured to generate a sensor signal;        and    -   b. a control circuit configured to cause the at least one sensor        to generate the sensor signal at a frequency that is related to        a telemedicine code.

26) An implantable medical device, comprising:

-   -   a. at least one sensor configured to generate a sensor signal;        and    -   b. a control circuit configured to cause the at least one sensor        to generate the sensor signal at a frequency that allows a        doctor to qualify for payment under a telemedicine insurance        code.

27) An implantable medical device, comprising:

-   -   a. at least one sensor configured to generate a sensor signal;        and    -   b. a control circuit configured to cause the at least one sensor        to generate the sensor signal at a frequency that allows a        doctor to qualify for full payment under a telemedicine        insurance code.

28) A method, comprising, generating a sensor signal that is related toan implanted medical device at a frequency that allows a doctor toqualify for payment available under a telemedicine insurance code.

29) A method, comprising, generating a sensor signal that is related toan implanted medical device at a frequency that allows a doctor toqualify for full payment available under a telemedicine insurance code.

30) An implantable prosthesis, comprising:

-   -   a. a housing; and    -   b. an implantable circuit disposed in the housing and configured        -   i. to generate at least one first signal representative of a            movement;        -   ii. to determine whether the signal meets at least one first            criterion; and        -   iii. to send the signal to a remote location in response to            determining that the signal meets the at least one first            criterion.

31) The implantable prosthesis of embodiment 30 wherein the housingincludes a tibial extension.

32) The implantable prosthesis of embodiment 30 wherein the movementincludes a movement of a patient.

33) The implantable prosthesis of embodiment 30 wherein the movementincludes a patient walking.

34) The implantable prosthesis of embodiment 30 wherein the at least onefirst criterion includes that the signal represents the movement for atleast a threshold duration.

35) The implantable prosthesis of embodiment 30 wherein the at least onefirst criterion includes that the signal represents the movement for atleast a threshold number of events.

36) The implantable prosthesis of embodiment 30 wherein:

-   -   a. the movement includes a patient walking; and    -   b. the at least one first criterion includes that the signal        represents the movement for at least a threshold number of steps        taken by the patient.

37) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured:

-   -   a. to determine whether the movement meets at least one second        criterion before determining whether the signal meets the at        least one first criterion; and    -   b. to determine whether the signal meets the at least one first        criterion in response to determining that the movement meets the        second criterion.

38) The implantable prosthesis of embodiment 37 wherein the at least onesecond criterion includes that the movement is a patient walking.

39) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured:

-   -   a. to determine, in response to the signal, whether the movement        meets at least one second criterion before determining whether        the signal meets the at least one first criterion; and    -   b. to determine whether the signal meets the at least one first        criterion in response to determining that the movement meets the        second criterion.

40) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured:

-   -   a. to determine, in response to the signal, whether the movement        meets at least one second criterion; and    -   b. to cease generating the signal in response to determining        that the movement does not meet the at least one second        criterion.

41) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured:

-   -   a. to determine, in response to the signal, whether the movement        meets at least one second criterion; and    -   b. to cease generating the signal before determining whether the        signal meets the at least one first criterion in response to        determining that the movement does not meet the at least one        second criterion.

42) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured:

-   -   a. to store the signal in response to determining that the        signal meets the at least one first criterion; and    -   b. to send the stored signal to the remote location.

43) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured to encrypt the signal before sending thesignal to the remote location.

44) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured to encode the signal before sending thesignal to the remote location.

45) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured:

-   -   a. to generate a message that includes the signal; and    -   b. wherein sending the signal includes sending the message.

46) The implantable prosthesis of embodiment 30 wherein the implantablecircuit is further configured:

-   -   a. to generate a data packet that includes the signal; and    -   b. wherein sending the message includes sending the data packet        to the remote location.

47) A base station, comprising:

-   -   a. a housing; and    -   b. a base-station circuit disposed in the housing and configured        -   i. to receive, from an implantable prosthesis, at least            first signal representative of a movement;        -   ii. to send the at least one first signal to a destination;        -   iii. to receive at least one second signal from a source;            and        -   iv. to send the at least one second signal to the            implantable prosthesis.

48) The base station of embodiment 47 wherein the base-station circuitis configured to poll the implantable prosthesis for the first signal.

49) The base station of embodiment 47 wherein the base-station circuitis configured to decrypt the at least one first signal before sendingthe at least one first signal to the destination.

50) The base station of embodiment 47 wherein the base-station circuitis configured to encrypt the at least one first signal before sendingthe at least one first signal to the destination.

51) The base station of embodiment 47 wherein the base-station circuitis configured to decode the at least one first signal before sending theat least one first signal to the destination.

52) The base station of embodiment 47 wherein the base-station circuitis configured to encode the at least one first signal before sending theat least one first signal to the destination.

53) A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a current through the fuse exceeding an overcurrent threshold.

54) A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a current through the fuse exceeding an overcurrent threshold for atleast a threshold time.

55) A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a voltage across the fuse exceeding an overvoltage threshold.

56) A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a voltage across the fuse exceeding an overvoltage threshold for atleast a threshold time.

57) A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a temperature exceeds an overtemperature threshold.

58) A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a temperature exceeding an overtemperature threshold for at least athreshold length of time.

59) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted; and    -   b. transmitting the sensor signal to a remote location.

60) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. sampling the sensor signal; and    -   c. transmitting the samples to a remote location.

61) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. determining whether the sensor signal represents a qualified        event; and    -   c. transmitting the signal to a remote location in response to        determining that the sensor signal represents a qualified event.

62) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. receiving a polling signal from a remote location; and    -   c. transmitting the sensor signal to the remote location in        response to the polling signal.

63) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. generating a message that includes the sensor signal or data        representative of the sensor signal; and    -   c. transmitting the message to a remote location.

64) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. generating a data packet that includes the sensor signal or        data representative of the sensor signal; and    -   c. transmitting the data packet to a remote location.

65) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. encrypting at least a portion of the sensor signal or data        representative of the sensor signal; and    -   c. transmitting the encrypted sensor signal to a remote        location.

66) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. encoding at least a portion of the sensor signal or data        representative of the sensor signal; and    -   c. transmitting the encoded sensor signal to a remote location.

67) A method, comprising:

-   -   a. generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   b. transmitting the sensor signal to a remote location; and    -   c. entering an implantable circuit associated with the        prosthesis into a lower-power mode after transmitting the sensor        signal.

68) A method, comprising:

-   -   a. generating a first sensor signal in response to a movement of        a subject in which a prosthesis is implanted;    -   b. transmitting the first sensor signal to a remote location;    -   c. entering at least one component of an implantable circuit        associated with the prosthesis into a lower-power mode after        transmitting the sensor signal; and    -   d. generating a second sensor signal in response to a movement        of the subject after an elapse of a low-power-mode time for        which the implantable circuit is configured.

69) A method, comprising:

-   -   a. receiving a sensor signal from a prosthesis implanted in a        subject; and    -   b. transmitting the received sensor signal to a destination.

70) A method, comprising:

-   -   a. sending an inquiry to a prosthesis implanted in a subject    -   b. receiving a sensor signal from a prosthesis after sending the        inquiry; and    -   c. transmitting the received sensor signal to a destination.

71) A method, comprising:

-   -   a. receiving a sensor signal and at least one identifier from a        prosthesis implanted in a subject;    -   b. determining whether the identifier is correct; and    -   c. transmitting the received sensor signal to a destination in        response to determining that the identifier is correct.

72) A method, comprising:

-   -   a. receiving a message including a sensor signal from a        prosthesis implanted in a subject;    -   b. decrypting at least a portion of the message; and    -   c. transmitting the decrypted message to a destination.

73) A method, comprising:

-   -   a. receiving a message including a sensor signal from a        prosthesis implanted in a subject;    -   b. decoding at least a portion of the message; and    -   c. transmitting the decoded message to a destination.

74) A method, comprising:

-   -   a. receiving a message including a sensor signal from a        prosthesis implanted in a subject;    -   b. encoding at least a portion of the message; and    -   c. transmitting the encoded message to a destination.

75) A method, comprising:

-   -   a. receiving a message including a sensor signal from a        prosthesis implanted in a subject;    -   b. encrypting at least a portion of the message; and    -   c. transmitting the encrypted message to a destination.

76) A method, comprising:

-   -   a. receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   b. decrypting at least a portion of the data packet; and    -   c. transmitting the decrypted data packet to a destination.

77) A method, comprising:

-   -   a. receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   b. decoding at least a portion of the data packet; and    -   c. transmitting the decoded data packet to a destination.

78) A method, comprising:

-   -   a. receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   b. encoding at least a portion of the data packet; and    -   c. transmitting the encoded data packet to a destination.

79) A method, comprising:

-   -   a. receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   b. encrypting at least a portion of the data packet; and    -   c. transmitting the encrypted data packet to a destination.

80) A method, comprising:

-   -   a. receiving a sensor signal from a prosthesis implanted in a        subject;    -   b. decrypting at least a portion of the sensor signal; and    -   c. transmitting the decrypted sensor signal to a destination.

81) A method, comprising:

-   -   a. receiving a sensor signal from a prosthesis implanted in a        subject;    -   b. decoding at least a portion of the sensor signal; and    -   c. transmitting the decoded sensor signal to a destination.

82) A method, comprising:

-   -   a. receiving a sensor signal from a prosthesis implanted in a        subject;    -   b. encoding at least a portion of the sensor signal; and    -   c. transmitting the encoded sensor signal to a destination.

83) A method, comprising:

-   -   a. receiving a sensor signal from a prosthesis implanted in a        subject;    -   b. encrypting at least a portion of the sensor signal; and    -   c. transmitting the encrypted sensor signal to a destination.

84) An implantable circuit for an implantable prosthesis.

85) An implanted or an implantable prosthesis, including an implantablecircuit.

86) An implanted, or an implantable prosthesis, including a fuse.

87) A base station for communication with an implanted, or animplantable, prosthesis.

D. Computer Systems for Analysis, Dissemination of Information,Ordering, and Supply: Processing IMU Data Recorded During PatientMonitoring

As discussed in previous sections of this document, a patient isintermittently monitored, at home, in a work environment, at a doctor'soffice, or in another environment frequently inhabited by the patient,by the sensors incorporated in an implant in combination with a basestation or another communications device. The sensor data is uploaded toa base station from the implant, temporarily stored within the basestation during accumulation of data during a patient-monitoring session,and subsequently transmitted from the base station to a data-processingapplication running within one or more standalone servers, data centers,or cloud-computing facilities. As also discussed in previous sections ofthis document, the data may be transferred by various different types ofcommunications media, associated communications devices and subsystems,and operating-system communications services and functionalities usingmany different types of data-transfer protocols. The data is encodedaccording to predetermined formats and digital-encoding conventions andencrypted. In the current section of this document, the monitoring datais assumed to be transmitted from the base station to thedata-processing application in a series of sequenced messages. It isassumed that the data includes a time sequence of encoded IMU datavectors, discussed in more detail below, a patient identifier, a deviceidentifier, configuration parameters for the IMU, and other informationneeded by the data-processing application to interpret the encoded IMUdata vectors, identify the patient and sensor-equipped implant,authorize receiving and processing of monitoring data from the patient,generate output results and output reports, and distribute the outputresults and output reports to various predetermined recipients, such asclinicians, insurance providers, and other such recipients. Inalternative implementations, the monitoring data may be transferred asone or more files by various file-transfer protocols and facilities,although, of course, file-transfer protocols are implemented abovemessage protocols. In certain implementations,patient-monitoring-session data may be received on various types ofoptical or electromagnetic data-storage devices physically transportedto a computer or computing facility in which the data-processingapplication runs.

The primary task of the data-ingress and monitoring-data-processingcomponents of the data-processing application is to convert raw sensordata output, during a monitoring session, by the sensors incorporated inan implant into a digitally-encoded, human-readable report and/ordigitally-encoded output results that may be forwarded to clinicians,insurance providers, and/or additional automated systems for furtherautomated processing tasks. In addition, the monitoring-data-processingcomponents of the data-processing application may raise variousdifferent types of events and alarms, based on the output results for amonitoring session, that may be handled by other components of thedata-processing application or by other applications concurrentlyrunning within the one or more computers or distributed computersystems.

There are a very large number of different approaches that can beundertaken, in different implementations, to analyze the raw sensor datain order to generate output results. One approach is next describedbelow with reference to FIGS. 28-37H. In alternative implementations,different and/or additional types of sensor data may be included in themonitoring data received by the data-processing application frommultiple different sensors and incorporated into the analysis. Forexample, an implant may include a temperature sensor, various types ofchemical sensors, acoustic sensors, and other types of sensors, the dataoutput from which may be useful for diagnosing many types of problemsand anomalies that occur in various different types of implants. Thecurrent discussion focuses on IMU data produced by an implantproximately located to a knee joint. An initial portion of the followingdiscussion is devoted to a discussion of the processing of IMU outputdata to generate a number of metrics that can be subsequently used toinfer the operational condition and characteristics of a prosthetic kneejoint as well as to infer characteristics of a patient's ambulation.

FIG. 28 illustrates a three-dimensional Cartesian coordinate space andthe representation of a point in the space by a vector. Thethree-dimensional coordinate space is defined by familiar x, y, and zcoordinate axes, 2201-2203, respectively. The location of a point p 2204in this space can be represented by a vector r 2205, with vectorcomponents r_(x), r_(y), and r_(z) corresponding to the lengths of theprojections of the vector onto the coordinate axes 2206-2208. Adifferent point q 2209 is associated with a different position vector2210. A vector-valued function of time, f(t), may return a positionvector for each point in time within the time domain of the function.One type of vector-valued function may return position vectors fordifferent points of time that describe a space curve, or trajectory,such as an object moving in space. Another type of discretevector-valued function may be a function that represents the output ofan IMU over time.

FIGS. 29A-B illustrate the data output by an IMU. The IMU can be thoughtof as a black-box device 2220 with a fixed internal coordinate system2222-2224 which outputs a time-ordered sequence of 6-dimensional vectors2226-2228, where ellipsis 2229 indicates continuation of the sequence.Ellipses are used to indicate additional elements of a sequence orseries throughout FIGS. 28-37H. Each 6-dimensional vector, such as avector 2226, includes three numerical indications 2230 of the linearaccelerations of the IMU in the directions of the three coordinate axesand three numerical indications 2232 of the rotational or angularvelocity about each of the three IMU coordinate axes. The vectors outputby the IMU are associated with sequence numbers, such as sequence number“1” 2234 associated with vector 2226. In general, the accelerations andangular velocities are sampled at regular intervals in time 2236-2237,so that the relative sampling time for each vector is a linear functionof the sequence number associated with the vector. The sampling rate aswell as the meaning of the numerical values is specified by IMUparameters, including fixed parameters and configuration, oroperational, parameters. As mentioned above, the data received by thedata-processing application includes sufficient information with respectto these parameters to decode the numerical values into accelerationsand angular velocities expressed in a particular set of units and todetermine the sampling interval. In the following discussion, it isassumed that the vector data has been processed, if needed, so that theangular-velocity and acceleration data refer to the same internalcoordinate system. It is also assumed that the sampling rate is uniformover the data. When the sampling rate is not uniform, then thesampling-rate-dependent portions of the analyses, discussed below, mayneed to be carried out piecewise over subsequences of the data-vectorIMU output with uniform sampling rates.

FIG. 29B illustrates one type of information that can be derived fromthe data-vector output of an IMU. As discussed above, the vectors outputby the IMU can be thought of as a vector-valued function of time 2240.This function can be converted, by a trajectory-reconstruction process2242, to produce a corresponding vector-valued function 2244 thatreturns a position vector for each point in the time domain of thefunction, thus describing a space-curve trajectory 2246 of the origin2248 of the IMU internal coordinate system that represents the motion ofthe origin of the IMU in space and time relative to the initial positionof the origin of the IMU internal coordinate system. The IMU-outputvector-valued function can also be converted by anorientation-reconstruction process 2252 to produce a correspondingvector-valued function 2254 that outputs orientation vectors thatdescribe the orientation, at any point in time, of the IMU internalcoordinate system with respect to the initial orientation of theinternal coordinate system 2250. When the real-world initial positionand orientation of the IMU are known, the space curve can be orientedwith respect to the real-world coordinate system and the relativeorientations produced by vector-valued function 2254 can be transformedinto orientations defined by the real-world coordinate system. Ofcourse, there are a variety of different real-world coordinate systems.As discussed below, the current analyses considers a coordinate systemin which the x axis is parallel to the ground and has a directionparallel to the direction in which a patient is walking, during themonitoring session, the z axis is perpendicular to the ground andparallel to the bilateral axis of symmetry of the patient, and the yaxis is normal to both the x and z axes. This coordinate system isreferred to as the “natural coordinate system” in the followingdiscussion. Many other coordinate systems can be used in alternativeimplementations, including coordinate systems fixed to a particularrigid part of a patient's body, and cylindrical or spherical coordinatesystems fixed to the patient or to a position and orientation withrespect to the Earth's surface.

The above-mentioned trajectory-reconstruction andorientation-reconstruction processes are not further discussed. Theseprocesses are well-known and are based on Newtonian mechanics, includingintegration of accelerations to produce velocities and integration oflinear and angular velocities to produce linear and angular distances.However, additional, sophisticated mathematical processes are employedin trajectory and orientation reconstruction. As with all interpretationof instrumental data, there are many sources of error, and the errorscan propagate and accumulate to produce significant variations betweenthe computed trajectories and orientations and those actuallyexperienced by the IMU during position and orientation monitoring by theIMU. When possible, additional data and information is used to detectand account for instrumental errors during the processing of IMU data.In the approach to IMU-data processing described below, the numericalvalues of the IMU output vectors are converted into numerical valuesthat express the accelerations and angular velocities with respect tothe natural coordinate system. One approach to carrying out thisconversion is discussed below.

FIGS. 30A-G illustrate complex space curves that represent motions andresolution of the complex space curves into component motions. FIG. 30Ashows a short section of a harmonic spatial trajectory within a volumeof three-dimensional Cartesian space. The harmonic trajectory 2260 iscontained within the xz plane that is coincident with the x and z axes2261-2262 and the origin 2264. This harmonic trajectory is expressed bythe vector-valued function 2266, which is a vector-valued function oftime 2268. This type of harmonic trajectory may be similar to, at a highlevel, the trajectory of an IMU within an implant proximal to a kneejoint during ambulation by a patient. FIG. 30B introduces an additionalmotion component to the motion, or trajectory, shown in FIG. 30A. Thenew motion component 2270 is a linear harmonic motion in the y directioncentered about the origin of the internal IMU coordinate system, and isexpressed by the vector-valued function 2272. A composite vector-valuedfunction 2274 that includes both the original trajectory 2276 shown inFIG. 30A and the new motion component 2278 is shown as curve 2280 withinspatial volume 2282. The new trajectory remains periodic with respect tothe x axis, but has a rather complex shape that features periodicdeviations, in the y direction, of a higher frequency and smalleramplitude than the periodic frequency and amplitude of the originalharmonic trajectory. FIG. 30C illustrates addition of a new linear,harmonic motion in the x direction 2292 the original trajectory 2292 toproduce a composite vector-valued function 2294 that represents thecomplex space curve 2296 shown within volume 2298. In this case, thecomplex space curve 2296 is planar, but includes periodic deviations inthe x direction of a higher frequency and smaller amplitude than thethan the periodic frequency and amplitude of original harmonic motionshown in FIG. 30A. FIG. 30D illustrates, using the same illustrationconventions as used in FIGS. 30A-C, a space curve 2300 that represents acomposite vector-valued function 2302 that includes the originalharmonic trajectory 2304 shown in FIG. 30A, the y-direction motioncomponent discussed above with reference to FIG. 30B, and thex-direction motion component discussed above with reference to FIG. 30C.Space curve 2300 is quite complex, even though representing a relativelysimple vector-valued function that combines only three componentmotions.

The trajectory of an IMU within a knee-joint implant during ambulationmay be an extremely complex space curve featuring many differentcomponent motions that oscillate at many different frequencies. Onemotion component may be the rotation of the implant about the knee jointas the lower leg rotates with respect to the upper leg during walking.Another component motion is the motion of the patient in the directionof walking. Combination of these two motions may produce a periodictrajectory in the xz plane of the natural coordinate system. However,there may be many other component motions, including lateral motions ofthe knee joint, component motions due to rocking of the patient'sbilateral axis during ambulation, and higher-frequency motions relatedto frictional forces within the knee joint and other components of thepatient's body as well as to the complex geometries of the patient'sbody components, and may additionally include high-frequency motions dueto vibration or jostling of the implants with respect to the patient'sbody due to loose fittings and other causes. As a result, the spatialtrajectory of the IMU may be far too complex to decompose into componentmotions by spatial-domain analytical techniques.

As shown in FIG. 30D, component motions that have higher frequencies andlower amplitudes than the base trajectory shown in FIG. 30A, when addedto the component motion responsible for the base trajectory, producerelatively fine-grained and complex deviations from the base trajectory.By contrast, additional component motions with the same frequency as themotion that produces the base trajectory tend to generate geometricalterations in the base trajectory. FIG. 30E illustrates the addition oftwo low-amplitude component motions 2310 and 2312 to the componentmotion 2314 that generates the base trajectory to produce a compositevector-valued function 2316 represented by space curve 2318 shown involume 2320. This new trajectory is clearly periodic and has the samefrequency as the base trajectory (2260 in FIG. 30A), but now has aslightly helical form rather than the planar form of the basetrajectory. The normal trajectory from ambulation may include numerousdifferent component motions, in addition to the primary rotational andtranslation walking motions, but with frequencies similar to theambulation frequency, and may thus have a somewhat complex form butwithout the finer-granularity complexities that arise fromhigher-frequency component motions.

FIG. 30F illustrates a trajectory corresponding to the vector-valuedfunction obtained by subtracting the base-trajectory function 2320 fromthe complex function 2316 discussed above with reference to FIG. 30E.The trajectory 2330 produced by the vector-valued function representingthe difference between the vector-valued function 2316 and the basevector-valued function 2320 is an ellipse. This is not surprising,since, by subtracting away the base trajectory, there is no longer atranslational motion component corresponding to movement of the patientalong a path in space as the patient walks. The elliptical trajectorymay have different orientations and eccentricities, depending on theparticular harmonic component motions that remain in the vector-valuedfunction representing the difference between the complex vector-valuedfunction and the base trajectory. When only one linear harmonic motioncomponent in the direction of a coordinate axis remains, the ellipticaltrajectory collapses into a line segment representing linear harmonicmotion. As shown in FIG. 30G, an elliptical trajectory 2340 can beprojected onto each of the natural coordinate axes to generate theamplitudes 2342-2344 of the sum of the component motions in the x, y,and x directions. Thus, a patient's ambulatory trajectory can bedescribed as a composite motion obtained by adding, to a basetrajectory, the x, y, and x amplitudes of an elliptical trajectoryrepresenting additional motion components of the same frequency as thefrequency of the ambulatory trajectory as well as x, y, and x amplitudesof elliptical trajectories representing additional motion components anadditional non-gait-frequency frequencies. As discussed below, Fourieranalysis is one technique that can be used to decompose a complexmulti-frequency-component-motion trajectory into component motions ofdifferent frequencies. When a range of frequencies, or a frequency band,is considered rather than a single frequency, the above-describedelliptical trajectories may become somewhat distorted, but can still beanalyzed, as discussed above with reference to FIG. 30G, to obtain x, y,and x amplitudes for the frequency band. A particular ellipticaltrajectory obtained for a particular frequency band may represent asingle rotational-motion component or multiple linear harmonic motioncomponents, so it is not possible to decompose a complex spatial curveinto exactly the set of motion components that correspond to theindividual motions of individual body parts and implant parts, but it ispossible to decompose a complex spatial curve into a set of x, y, and xamplitudes for each of multiple different frequency bands that, whenrecombined, produce a motion associated with a trajectory very similarto the original measured trajectory. The x, y, and x amplitudes for eachof multiple different frequency band can serve as a very detailed andreliable numerical fingerprint for many different types of trajectoriesresulting from particular problems, pathologies, and other causessuperimposed on a basis gait profile, or trajectory.

Other types of techniques, including wavelets, may be used instead of,or in addition to, Fourier techniques and, in certain cases, may havesignificant advantages over Fourier techniques. As one example, the manydifferent higher-frequency motion components may be periodic, but theiramplitudes may decrease and increase periodically at lower frequencies.A loose implant screw, for example, may result in relativelyhigh-frequency vibrations, but only during relatively short periods oftime following each heel strike or knee rotation. Thus, additionalanalytical methods, including wavelets, may be useful in correlatinghigher-frequency motion components with lower-frequency gait-relatedevents. These techniques may be used to, for example, provideindications that a higher-frequency motion component is stronglycorrelated with heel strikes, maximum knee rotations, and othergait-related events. These types of correlations, in turn, may be usefulin resolving higher-frequency motion components into underlying,physiology based linear harmonics.

The resolution of a periodic space curve into component harmonicmotions, discussed above with reference to FIGS. 30A-G, provides a typeof numerical fingerprint for the component harmonic motions of theperiodic space curve. However, there may be non-periodic motions, suchas occasional slippages of an implant or non-periodic musclecontractions. FIG. 31 illustrates one method for dealing with varioustypes of non-periodic motions. Consider the space curve 2350 plotted inthree dimensions 2352 in FIG. 31. This curve is generally continuous butincludes a short linear section 2354 that may represent a suddenslippage of the implant containing the IMU. This type of non-periodicmotion can be recognized by a pair of discontinuities 2356 and 2357 inthe space curve. Because an IMU discretely samples accelerations androtational velocities, the space curves obtained from IMU data aregenerally discrete, rather than continuous, although continuous curvescan be obtained by various types of interpolation. A small portion 2358of space curve 2350 near discontinuity 2356 is shown at the top of FIG.31 at much higher resolution. Individual points of the discrete curveare represented by dots, such as dots 2360-2361. The resolution issufficiently high that the portions of the curve 2362 and 2363 appearnearly linear. The intersection of these two linear portions produces anintersection angle 2364 with an apex at the discontinuity. A point in atrajectory can be identified as a discontinuity when the interactionangle for best-fit line segments for two series of points preceding andfollowing the point is greater than a threshold value 2365. Adiscontinuity operator can be mathematically moved along a trajectory toidentify pairs of discontinuities 2366-2367 that define a non-periodicmotion, such as a shift or slip 2368. The average velocity in each ofthe component directions can be computed 2370, along with the distanceof the non-periodic motion, for such non-periodic motions bracketed bydiscontinuities in order to characterize the severity of the slip orshift.

FIGS. 32A-F illustrates the principle-component-analysis method that isused to rotate an initial coordinate system to a coordinate system inwhich the axes are aligned with the distributions of points representingexperimental observations. The principle component analysis method isfrequently used in data analysis. Each observation is a vector of metricdata values. FIG. 32A illustrates the equivalence between an observationmade at a particular time point and a P-dimensional vector in aP-dimensional space. In the example shown in FIG. 32A, there are onlythree metrics S1, S2, and S3, and thus P=3. Each metric is considered tobe a dimension, and so the three Cartesian axes 2382, 2383, and 2384 areeach assigned to one of the metrics. Each observation is a tuple ofthree metric data values 2386 which, when used as components of avector, describes a vector 2388 in the P-dimensional metric space.

FIG. 32B representation of observations, each consisting of a set ofmetric data values for each data source obtained at, or calculated for,a particular time point, as a matrix. Each row of metric data values,such as row 2392, for a particular time point, such as time point t_(i)2394, may be considered to be a P-dimensional vector 2396, referred toas an “observation.” A sequence of N observations can be organized as anN×P matrix {tilde over (X)}^(T) 2398 in which each row represents anobservation and in which each column represents a time sequence of datavalues for a particular metric. Again, the time point corresponding toan observation is inferred from the row index of the observation sincethe observations represent a time sequence with a uniform time intervalbetween successive observations. Alternatively, the transpose of matrix{tilde over (X)}, {tilde over (X)}^(T) 400, can be considered to includecolumn vectors representing observations.

FIG. 32C illustrates scaling and normalization of the set ofobservations represented by the matrix {tilde over (X)}. Severalstatistical parameters are computed for each time sequence of metricdata values for particular metrics, such as the metric data values forthe second metric contained in the second column 2402 of the matrix{tilde over (X)} 2404, including the average μ_(j) 406, the varianceσ_(j) ² 408, and the standard deviation σ_(j) 410. Then, for each columnj, each metric data value in the column can be scaled and normalized bysubtracting the average metric data value from the metric data value anddividing by the standard deviation 2412. When this is done for everyelement in the matrix, a scaled and normalized matrix X 2414 isproduced.

3×3 matrix A 2422 and a column vector u 2424 are shown at the top ofFIG. 32D. When u is an eigenvector of the matrix A, then equation 2426expresses the relationship of the eigenvector u and its correspondingeigenvalue λ

A−λI, where I is the identity matrix, by the column vector 0 2434 orthat the inverse of the matrix A−λI does not exist, as expressed by thefact that the determinant of this matrix is 0 2436. Only the latterproposition is reasonable, which indicates that, by solving thepolynomial equation 2444 shown in FIG. 32E, obtained from the expression2436 via expansion 2442 of expression 2436, the eigenvalues for thematrix A can be found. Because the polynomial equation 2444 is of order3, the dimension of u, there are generally 3 eigenvalues, although oneor more of the roots of equation 2444 may be degenerate. The matrixequation 2446 expresses the relationship between the matrix A, a matrixU in which each column is one of the eigenvectors of the matrix A, andthe matrix Λ, which is a diagonal matrix in which the elements along thediagonal are the eigenvalues of the matrix A in the order of thecorresponding eigenvectors in the matrix U. Multiplying each side ofequation 2446 from the right by the inverse of matrix U, U⁻¹, producesequation 2448. When the matrix A is the product of a matrix X and itstranspose X^(T), as shown in expression 2450, the eigenvalues of matrixare positive real numbers 2451, the eigenvectors of matrix areorthogonal 2452 when their corresponding eigenvalues are not equal, andthe inverse of matrix U, U⁻¹, is equal to the transpose of matrix U,U^(T) 453. Thus, when matrix A is the product of a matrix X and itstranspose X^(T), matrix A is equal to the matrix Λ multiplied from theleft by the matrix U and multiplied from the right by the transpose ofmatrix U, U^(T). While a 3×3 matrix example is used in FIGS. 32D and32E, the above-described characteristics of eigenvectors and eigenvaluesapply to matrices of arbitrary dimension.

X^(T), in the case of a three-dimensional metric space, such as thatshown in FIG. 32A, may fall within an ellipsoidal volume 2464 within thethree-dimensional metric space. As shown in plot 2462 of FIG. 32F, theellipsoidal volume has major and minor axes that are not coincident withthe axes corresponding to metrics S1 2466, S2 2467, and S3 2468. Abasis-vector change, equivalent to a set of coordinate changes, may bedesired so that a set of new coordinate axes, corresponding to what isreferred to as “principal components,” (“PCs”), can be found. The newcoordinate axes are aligned with the major and minor axes of theellipsoidal volume representing the distribution of observations inthree-dimensional space. Moreover, principal component PC1 2470 isaligned with the major axis of the ellipsoidal volume, principalcomponent PC2 is aligned with the longer of the two minor axes 2471 ofthe ellipsoidal volume, and principal component PC3 2472 is aligned withthe shorter of the two minor axes of the ellipsoidal volume. The basisvectors corresponding to the principal components of the new coordinateaxes are contained as columns in a matrix Q 2476. The principalcomponents correspond to the directions of greatest variability withinthe ellipsoidal volume in decreasing order of variability and the basisvectors corresponding to the principal components are orthogonal. Ingeneral, the bulk of the variability within a distribution ofobservations can be largely explained in terms of, or expressed as afunction of, an initial subset of the principal components. For example,in the distribution shown in FIG. 32F, were the ellipsoidal volumeprojected onto a plane normal to the third principal component 2472, themajority of the variability in the distribution of observations would beapparent in the resulting two-dimensional ellipsoid with major axiscorresponding to the first principal component 2470 and minor axiscorresponding to the second principal component 2471. In essence, theprincipal components can be viewed as a new set of metrics each derivedfrom the original metrics as a linear combination of the originalmetrics. The data values corresponding to the new set of metrics,contained in a factor score matrix F, which is defined to be generatedfrom the original metric data values stored in the matrix X bymultiplying the matrix X from the right by the matrix Q, which containsthe principal components as column vectors 2478, under the constraintsthat the matrix F^(T)F=Q^(T)X^(T)XQ is a diagonal matrix 2480 and thatthe matrix Q is orthogonal 2482. By using the technique of Lagrangianmultipliers, it can be shown that X^(T)X=QΛQ^(T) 484, where Λ is adiagonal matrix of Lagrangian multipliers, which leads to expression2486. Thus, determining the principal components, which is equivalent todetermining the matrix Q, reduces to a problem of determining theeigenvectors and eigenvalues of the matrix X^(T)X. With the matrix Q inhand, the coordinate transformation that takes the original scaled andnormalized metric data values in the matrix X to the data values for anew set of metrics referred to as principal components, stored in thematrix F, is carried out by multiplying the matrix X from the right bythe matrix Q, as expressed in expression 2478.

FIG. 33 illustrates use of principal component analysis to determine thenatural coordinate system based on raw or filtered IMU output data. Thenatural coordinate system 2480-482 is shown in FIG. 33 aligned with abase trajectory 2484 and the ground 2486. Generally, a patient isvertical while walking, and the patient's legs move primarily in avertical plane 2488. Therefore, most of the linear accelerations areparallel or nearly parallel to this plane. Because the patient is movingalong a path, in the x direction, principal component analysis, whenapplied to the linear-acceleration components of the IMU output data,determines the x axis of the natural coordinate system as the principalaxis. The z axis of the natural coordinate system is determined to bethe secondary principal axis, and the y axis of the natural coordinatesystem is the tertiary principal axis, since few linear accelerationsshould have y components. Similarly, most of the angular velocitiesshould be in the xz plane perpendicular to the y axis during walking,and principal component analysis, when applied to the angular-velocitycomponents of the IMU output data, determines the y axis of the naturalcoordinate system to be the principal axis. Filtering the IMU outputdata to retain only the walking-cycle-frequency data components mayprovide greater reliability to the principal axes determinations byprincipal component analysis. A rotation matrix can be then determinedthat, when applied, by matrix multiplication, to the IMU output datavectors convert the numerical values within the data vectors tonumerical values corresponding to the natural coordinate system. Inalternative implementations, the spatial trajectory produced byprocessing IMU output data can be rotated in three-dimensional space toalign the base-trajectory plane to the vertical plane, and a rotationmatrix can then be derived from the rotations needed to make thisalignment. Other types of implementations are possible.

FIGS. 34A-D illustrate forward and inverse Fourier transforms. As shownin FIG. 34A, a continuous function of a real variable 2490 can betransformed by a forward Fourier transform 2492 to a function of afrequency variable 2494. The inverse Fourier transform 2496 transformsthe function of the frequency variable back to the original function2498. The function of the frequency variable generally produces complexvalues, having both real and imaginary components 2499. The valuesproduced by the function of the frequency variable can each bealternately represented by the product of a magnitude, or modulus, and aphase angle 2500. Often, the square of the absolute values of thecomplex values produced by a Fourier transform of a function of a realvariable are plotted to visualize the Fourier transform, thevisualization referred to as a “power spectrum” 2502. The complexexponential term in the Fourier transform, viewed as a sum of n discretereal-variable values 2504, is equivalent to n harmonics 2506, whichillustrates the fact that a Fourier transform can be thought of as thelimit of the sum of an infinite number of harmonics.

In the lower portion of FIG. 34A, an expression for an example functionof a real value 2508 and a corresponding plot of the function 2509 areshown. Computation of the Fourier transform of the function isillustrated by expressions 2510 and plot 2511 shows a plot of theabsolute value of the Fourier-transform, a function of the frequencyvariable u. Expressions 2512 at the top of FIG. 34B illustrate a Fouriertransform and inverse Fourier transform for a function of two realvariables 2514. An example two-variable function 2516 and itscorresponding Fourier transform 2518 are shown in the middle of FIG.34B. Often, as illustrated by plot 2520 in FIG. 34B, a function isdiscrete, representing samples of the y values produced by the functionfor discrete values of the domain 2522-2526. The forward and inverseFourier transforms for a discrete function of a single real variable areshown by expressions 2530 in FIG. 34B. Forward and inverse Fouriertransforms for a discrete function of two real variables are shown inexpressions 2532.

Fourier transforms are used widely in mathematics and all branches ofquantitative science for many different purposes. FIGS. 34C-D illustratehow Fourier transforms can be used to filter frequency components from aperiodic function. Plot 2540 is a graphical representation of the cosinefunction 2542. Plot 2544 is a graphical representation of a harmonicfunction 2546 with a frequency 3 times greater than that of function2542. Plot 2548 is a graphical representation of the composite function2550 obtained by adding functions 2542 and 2546. Just as in the examplesof adding space curves representing different types of harmonic motion,discussed above with reference to FIGS. 30A-30G, the graph of thecomposite function 2550 has a somewhat complicated form. When manydifferent types of harmonic functions are added together, the form canbe extremely complicated. In order to decompose such complicatedfunctions, Fourier transforms are employed. Plot 2560 in FIG. 34D is agraphical representation of the absolute value of the Fourier transformof function 2550 represented by plot 2548 in FIG. 34C. The Fouriertransform plot contains four points, or vertical line segments 2562-565.Vertical line segments 2562 and 2565 occur at the frequencies −3 and 3,while vertical line segments 2563 and 2564 occur at frequencies −1and 1. Thus, the plot indicates that there are two harmonic componentsof function 2550, one with the frequency of the base harmonic of thecomposite function and one with a frequency three times greater than thebase frequency. In order to recover the latter component, thebase-frequency values of the Fourier transform can be removed, as shownin plot 2566 and represented by expressions 2568, to generate a newfunction of a frequency variable. When inverse Fourier transform isapplied to this new function of a frequency variable, the resultingreal-valued function 2570, graphically represented by plot 2572, is theharmonic component of function 2550 with a frequency equal to threetimes the base frequency. Thus, to select a desired harmonic componentof a composite function with many different harmonic components, thecomposite function can be Fourier transformed to the frequency domain,all of the values for frequencies other than the frequency of thedesired harmonic component are set to 0, and the alteredfrequency-domain function can then be inverse Fourier transformed toproduce the desired harmonic component of the original complex function.This process is referred to as “bandpass filtering.”

FIG. 35 illustrates the use of Fourier transforms on the data-vectoroutput of the IMU. The vector-valued function representing the IMU dataoutput 2580 can be decomposed into six functions 2582 that return singlefloating-point values. Each of the six functions returns the numericalvalue of one of the components of the 6-dimensional vectors output bythe IMU. These discrete functions can be Fourier transformed to thefrequency domain 2584, the frequency-domain functions can be filteredfor a particular frequency 2586, and the inverse Fourier transform thenapplied to return the harmonic component of the desired frequency of theoriginal function 2588. A filtered vector-valued function 2590 for theIMU data can then be obtained by adding together all of the filteredfloating-point-valued functions. The above-discussedtrajectory-reconstruction process can then be carried out on thefiltered vector-valued function 2592 to produce a trajectory 2594 forthe component harmonics of the output data at the specified frequency.In general, as discussed above, the trajectory will be an ellipse 2596from which the x, y, and z amplitudes for the sum of the harmoniccomponents at the specified frequency can be determined by projection2598. There may be additional complexities associated with theangular-velocity-angle data, but, in general, it is possible usingbandpass filtering to isolate the component motion trajectories of theoverall trajectory represented by data vectors output by an IMU.

FIGS. 36A-B illustrate the data output by the data-processingapplication as a result of processing and analyzing the raw data,obtained during a monitoring session, that is received from a basestation. The output data includes patient and sensor data 2600, such asa patient ID 2602, a date 2604 and time 2606, sensor-configurationparameters 2608, and a reference to the compressed raw data archivedwithin the computing facility 2609. There may be a great deal ofadditional patient and sensor data included, depending on theimplementation. A next block of data 2610 output by the data-processingapplication contains various parameters and metrics that representcharacteristics of the patient's gait. These may include the distancetraveled during the recorded monitoring session 2611, the number of gaitcycle 2612 observed, the average stride distance 2613, the averagelinear velocity of the patient while walking 2614, additional results2615 that can be obtained by analyzing the gait-frequency spatialtrajectory, and a reference to the basis gait profile 2616 for thepatient. The basis gait profile may be a gait trajectory previouslyrecorded for the patient or may be selected, based on the patient'sphysical characteristics, from a set of standard basis gait profiles.The patient's gait is next represented 2620 by the x, y, z amplitudescomputed from the elliptical trajectory obtained by subtracting thebasis gait profile or trajectory from the gait-frequency trajectoryobserved in the monitoring session, obtained by bandpass filtering,along with the basis gait profile, as discussed above with reference toFIGS. 30E-G. Next, all the various non-gait-frequency motion modesdetected by bandpass filtering of the IMU data and trajectoryreconstruction are represented by the x, y, z amplitudes computed fromthe elliptical trajectories generated from the bandpass-filtered IMUdata 2624-2626. This data includes an indication of the number ofdetected non-gait-frequency motion modes 2628 followed byrepresentations of the motion modes, each of which includes thefrequency of the motion mode, such as frequency 2634 in motion-mode datablock 2624, along with the x, y, z amplitudes, such as amplitude 2632for motion mode 2624. The representation of each motion mode also mayinclude a field, such as field 2634 for motion mode 2624, indicatingwhether or not the motion mode was first detected in the data outputfrom the currently considered monitoring session. Next, the output dataincludes an indication of the number of detected discontinuities in thereconstructed gait cycle 2636 and a representation of each of thesediscontinuities 2638-2639. The representations, such as representation2638, may include indications of the times and distance traveled by thepatient during the slip or shift bracketed by the two discontinuities2640 and 2642 as well as indications of the velocities of thenon-periodic motion in the x, y, z directions 2644. Then, as shown inFIG. 36B, the current results obtained by data analysis for themonitoring session are compared against the results from a previousmonitoring session as well as to the running average of the results forall of the monitoring sessions up to the current point in time. Thisresults in pairs of Δ values for many of the metrics shown in FIG. 36A,each pair including the difference between the current result for eachmetric and the result for the metric obtained in the previous monitoringsession, referred to as a Δ_(t-1) value for the metric, and thedifference between the current result for the metric and the runningaverage for the metric over all or a most recent subset of the previousmonitoring sessions, referred to as Δ_(average). Pairs of Δ values areproduced for the gait-characteristics data 2650, the gait-frequencyadditional motion modes 2652, the non-gait-frequency motion modes 2654,the number of discontinuities 2654, and the maximum observeddiscontinuity velocities 2656. As mentioned above, the results may alsoinclude metrics that indicate correlations between different motioncomponents, such as correlations between higher-frequency motion modeswith gait-cycle events, such as heel strikes, maximum extensions of thelower leg, change in rotational direction of the knee, and other suchevents.

In many cases, data may be acquired, during monitoring sessions, frommultiple sensors. There may be, for example, multiple IMU sensors inmultiple implants within a patient's body, such as implants in the bonesabove and below and artificial knee joint. In other cases, there may bemultiple sensors of different types. In these cases, there may be a setof output results, discussed above with reference to FIGS. 36A-B, thatinclude output results for each of the different sensors. In addition,time correlations between the output results for multiple sensors may beincluded as an additional output result. As one example, a lower-leg IMUbased sensor may detect a high-frequency motion component that alwaysfollows, in time, a motion component detected by an upper-leg IMU basedsensor of a different frequency. This might be indicative of aninstability in the upper leg that propagates through the knee to thelower leg, or may, instead, represent two events correlated with amotion within the knee joint.

FIG. 36C illustrates a final portion of the results generated by thedata-processing application. All of the various results obtained fromthe analysis of the monitoring-session data 2660 can be considered to bea set of parameters 2662. These parameters can be input to a decisiontree 2664 that analyzes the parameters in order to determine what thedata appear to indicate about the state of the implant in the state ofthe patient. The decision tree may contain many levels of nodes, formore than those shown in FIG. 36C, each of which represents a decisionas to what subcategories of implant state and patient state may beindicated by the data results. In the leaf nodes of the decision tree,such as leaf node 2666, a portion of the parameters may be input to aneural network 2668 or some other type of machine-learning orpattern-recognition entity to derive more detailed inferences andsuggestions with regard to any problems or anomalies detected in themonitoring session and how these problems or anomalies might beaddressed by therapy, additional equipment, or other interventions. Theresults of this analysis are output as additional report components,such as additional report component 2670, containing higher-levelanalytical result and inferences. For example, based on the particularharmonic motion modes observed in the monitoring session, along with thevarious Δ_(t-1) and Δ_(average) data, the additional report may includean inference that a particular implant screw has loosened, as a resultof which the lower leg exhibits a rotational vibration during walking,and may suggest that this problem may be addressed either by anadditional external prosthesis, by surgery, or by other interventions.This additional report component containing higher-level analysis andinferences is packaged, along with the output data discussed above withreference to FIGS. 36A-B, as the output report and output data generatedby the data-processing application for the received monitoring-sessiondata.

FIGS. 37A-H provide control-flow diagrams that illustrate the currentlydiscussed implementation of the data-processing application thatprocesses patient-monitoring-session data. FIG. 37A shows a control-flowdiagram for the data-ingress component of the data-processingapplication. This component may run on one or more frontend serverswithin a cloud-computing facility that receive communications fromexternal computer systems. In step 2671, on power up, the data-ingresscomponent initializes communications support, database connections, andinternal server connections, and carries out other types ofinitializations to prepare for receiving data messages from externalcomputers. In step 2672, the data-ingress component waits for a nextevent to occur. When the next event is a new data-transmission event, asdetermined in step 2673, a new-data handler is called, in step 2674, tohandle the new data transmission. When the next event is adata-transmission-completion event, as determined in step 2675, acomplete-data handler is called, in step 2676, to complete reception ofa data transmission. Many other different types of events may behandled, such as a handler for all but the last of the additionalmessages in a data transmission and a handler for anadministration-update event which, as determined in step 2677, ishandled by calling and administration handler 2678. A default handler2679 handles any rare or unexpected events. When there are more eventsqueued for handling, as determined in step 2680, control returns to step2673 to process a next event. Otherwise, control returns to step 2672,where the data-ingress component waits for a next event to occur.

FIG. 37B provides a control-flow diagram for the handler “new data,”called in step 2674 in FIG. 37A. In step 2682, the new-data handlerreceives the new-data message and parses the message to receive the userID, device ID, and other such information. In step 2683, the new-datahandler accesses data storage to verify and authenticate the datamessage. For example, the user ID and device ID should correspond to apatient and the patient's implant recorded in a database table or file.Authentication may also involve passwords, encryption keys, and othertypes of security data and corresponding security measures to ensurethat only legitimate data messages are processed. If the new-datamessage fails authentication, as determined in step 2684, the new-datahandler invokes various error-handling procedures 2685. In the case thatthe error-handling procedures manage to authenticate the data message,as determined in step 2686, or when the message was initiallyauthenticated control flows to step 2687. Otherwise, in step 2688, thenew-data handler transmits a message-declined response to the sender. Instep 2687, the new-data handler creates a new transaction ID for thedata transmission, allocates a new data buffer indexed by thetransaction ID, stores a transaction-ID/user-ID/device-ID triple in apending-data lookup table, along with a timestamp, and stores theinitial portion of the data transmission contained in the data messagein the data buffer allocated for the data transmission. In step 2690,the new-data handler transmits an acknowledgment message to the basestation from which the message was received.

FIG. 37C provides a control-flow diagram for the complete-data handlercalled in step 2676 of FIG. 37A. In step 2692, the complete-data handlerreceives a final message in a data transmission and extracts identifyinginformation from the message. In step 2693, the complete-data handlerchecks the lookup table for the user ID and device ID and retrieves thetransaction ID associated with the user ID and device ID. If thetransaction ID cannot be found, as determined in step 2694, thecomplete-data handler undertakes error handling, in step 2695. If atransaction ID is found, as determined in step 2696, control flows tostep 2698, as it does when the transaction ID is initially found.Otherwise, in step 2697, a message-declined message is transmitted backto the base station. The complete-data handler stores the data containedin the data message into the data buffer to complete the datatransmission, in step 2698, determines a backend data-processing serverfor processing the data, in step 2699, logs a data-transition-completionevent in a log file, in step 2700, archives the transmitted data in step2701, transmits a data-processing request to the selected backend serverin step 2702, and, finally, adds a timestamp to the lookup table entryfor the data transmission and returns an acknowledgment to the basestation in step 2703.

FIG. 37D provides a control-flow diagram for a data-processing routineexecuted by a backend server to process a data-processing request sentto the backend server by the data-ingress server, or frontend server,discussed above with reference to FIGS. 37A-C. Like the front-endserver, the backend server can be viewed as implemented above anunderlying event loop, similar to the event loop discussed above withreference to FIG. 37A. The data-processing routine is called from theunderlying event loop to handle a newly receiveddata-processing-request. In step 2710, the data-processing routinereceives the data-processing request from the front-end server andparses the contents of the request. In step 2711, the data-processingroutine uses the patient ID and device ID included in thedata-processing request to access patient and device information in thedata store as well as to obtain the transaction ID for the datatransmission, in the case that the transaction ID is not included in thedata-processing request, in order to access the data buffer containingthe patient-monitoring-session data received by the front-end server. Instep 2712, the data-processing routine calls the routine “processing” toprocess the patient-monitoring-session data indexed by the transactionID. When the processing routine returns an indication of successfulprocessing, as determined in step 2713, the data-processing routine, instep 2714, distributes the output report and output data to a list ofrecipients for the output report and output data, and additionallystores the reporting output data in the data store. As discussed above,the analysis-output data generated for a previous patient-monitoringsession is employed during generation of the analysis-output data forthe subsequent patient monitoring session. Ultimately, the stored outputreports and output data are archived after some threshold period oftime. In step 2715, the data-processing routine enters aprocessing-completion entry into a log file, removes the lookup-tableentry associated with the data transmission, deallocates thetransaction-ID-index data buffer in which the data was stored by thefront-end server, and updates running averages of metrics for thepatient and implant.

Finally, in step 2716, the data-processing routine may raise variousevents and alarms based on the content of the report and output datafrom data processing and analysis of the patient-monitoring-sessiondata. A variety of different types of events and alarms may be raised.As one example, the report may indicate that a serious problem hasdeveloped that needs immediate attention, in which case an alert may beraised for handling by other components of the data-processingapplication or other applications executing within the computer system.These components may transmit messages to the base station, which mayinclude output devices that alert the patient of the need to contact amedical practitioner or that may directly alert one or more medicalpractitioners or emergency services. As another example, when the reportindicates the need for additional prosthetic equipment, or other typesof additional equipment or services, an event may be raised that can behandled by other components of the data-processing application or otherapplications executing within the computer system to arrange for theadditional equipment to be purchased by or on behalf of the patient andthe additional services provided to the patient. Thus, the reports anddata output from the data processing may be the basis not only ofinforming medical practitioners of the current patient and devicestates, but may also be the basis for provision of many additional typesof services related to the states of the patient and device in order toassist the patient.

FIGS. 37E-H provide control-flow diagrams for the processing routinecalled in step 2712 of FIG. 37D. The processing routine employs thevarious different types of analytic tools discussed above with referenceto FIGS. 28-35. It is assumed, for compactness of illustration anddescription, that only data for a single sensor is included in the datatransmission. In the case that data for multiple sensors is included ina data transmission, the data-processing routine includes a higher-levellooping control structure to control processing data for each of themultiple sensors, and the data processing routine may additionallyinclude logic for correlating component motions with gait-cycle eventsand correlating component motions detected by different sensors, asdiscussed above. In step 2728, the processing routine calls a routine“qualify data” to generate various different metrics from the raw IMUdata and/or from an initial trajectory computed from that data todetermine whether or not the transmitted data corresponds to the periodof time of sufficient length, during which the patient is walking, forapplication of the above-discussed analysis methods. These initialmetrics generally are sufficient to recognize the gait cycle anddetermine the gait-cycle frequency, and may provide evenfiner-granularity information with regard to the patient's ambulation,in the case that the data corresponds to a period of patient ambulation.When, as determined in step 2721, the data appears to be sufficient foranalysis, as determined from the return value output by the qualify-dataroutine in step 2720, control flows to step 2723, where the dataanalysis begins. Otherwise, the processing routine returns a failureindication, in step 2722. When the data is qualified, the deviceinformation and sensor-configuration information included in thetransmitted data is used to scale, synchronize, and normalize thedata-vector sequence output by the IMU and determine the samplinginterval, in step 2723. In certain implementations, the raw data may befiltered to detect erroneous data values resulting from transmissionerrors. In step 2724, a series of n bandpass filters, discussed abovewith reference to FIG. 34D, are generated or retrieved from the datastore. Each of the bandpass filters is used to recover datacorresponding to a particular relatively narrow frequency range. In step2725, the IMU data is filtered to recover the gait-frequency data,including both limb rotation as well as movement of the patient along awalking path, and principal component analysis is applied to thegait-frequency data to determine the natural coordinate system, asdiscussed above with reference to FIG. 33. In step 2727, a rotationmatrix is generated for transformation of the numerical values in thenumerical values that would be produced by an IMU aligned with thenatural coordinate system. In the for-loop of steps 2728-2730, thevarious bandpass filters generated or retrieved in step 2724 aresuccessively applied to the raw IMU data in order to recover IMU datafor each of the frequency ranges selected by the bandpass filters. Ofcourse, one of those filters likely corresponds to the gait-cyclefrequency, in which case the filtering and data recovery for thatfrequency has already been carried out, in step 2725. Next, turning toFIG. 37F, spatial trajectories are reconstructed for each of thefrequency ranges in the for-loop of steps 2736-2739. Additionally, aninitial full-data trajectory reconstruction T is also performed. In step2740-2742, the trajectory representing the difference between theobserved patient gait and a basis gait profile is generated, asdiscussed above with reference to FIGS. 30E-F. In step 2743, thegait-characteristics output data is obtained from the trajectorycomputed from the gait-frequency-data and aligned and scaled withrespect to the basis gait profile, in step 2741, included as thegait-characteristics data 2610 in the output report. In step 2744, thex, y, z amplitudes for the trajectory representing the differencebetween the observed patient gait and the basis gait profile arecomputed to produce the harmonic motion modes that represent thedepartures of the observed patient gait from the basis gait profile,included as the Δgait motion modes 2620 in the output report. In thefor-loop of steps 2745-2748, the harmonic non-gait-frequency motionmodes are computed from the non-gait-frequency trajectories computed inthe for-loop of steps 2736-2739. Turning to FIG. 37G, only thenon-gait-frequency modes with an overall amplitude greater than athreshold value are selected for reporting. These are reported in thenon-gait-frequency motion modes (624-626 in FIG. 36A). In steps2751-2752, the discontinuities in the gait trajectory, discussed abovewith reference to FIG. 31, are determined and those with overallvelocities or displacements greater than a threshold value are selectedfor reporting (638-639 in FIG. 36A). Additional output values, such asthe number of non-gait-frequency motion modes reported and the number ofdiscontinuities reported are computed in step 2753. In step 2754, themost recent output report is retrieved from the data store for thepatient, as well as running averages for the various computed metricsdiscussed above. Then, in the for-loop of steps 2755-2757, theabove-discussed Δ_(t-1)/Δ_(average) pairs for each metric are computedfor the portion of the output report illustrated in FIG. 36B. Turning toFIG. 37H, all of the computed metrics obtained by analysis of thepatient-monitoring-session data are collected as a set of parametersthat are submitted to the decision tree, in step 2761, to obtain thediagnoses/suggestions report discussed above with reference to FIG. 36C.The computed metrics, the diagnoses/suggestions report, and otherinformation, including device and patient information, are then packagedtogether, in step 2762, as the output report and output data generatedby the data-processing system in response to receiving thepatient-monitoring-session data.

Using the methodology described above, in one aspect, the presentdisclosure provides algorithm features for discriminating instabilitysignature from kinematic motion and degradation anomalies (includingincomplete osteo-integration). These algorithms will make use of dataobtained over a defined spectral specification of about 10 Hz to about120 Hz, and a temporal specificity of about 0.05-0.5 seconds. Inaddition, the data generated by the sensor will provide some directionalspecificity. For example, medial-lateral instability may be observedfrom data obtained by a y-axis accelerometer and a z-axis gyroscope,where these two data sets may optionally be correlated or multiplied toincrease specificity (referred to as sensor fusion). As another example,anterior-posterior instability may be observed from data obtained by az-axis accelerometer and a y-axis gyroscope, where again these two datasets may optionally be correlated or multiplied to increase specificity.

In one embodiment, the methods of the present disclosure make use ofsensor fusion, which refers to the combination or correlation ofdifferent sensor inputs that qualify or increase algorithmconfidence/performance, and help to reject external noise. Possiblenoise sources that could introduce anomalies overlapping or interferingwith “instability signature” might be riding in a car. Example of sensorfusion to reject car vibrations: correlate and qualify “instabilitysignature” as occurring repeatedly at specific points of normalkinematic motion; car noise/vibration will be random or occurring in theabsence of normal kinematic motion; instability will correlate toinflection points of normal kinematic motion—for instance a lateral(medial-lateral) instability will be detected by y-axis accelerometerfor instability signature and will correlate in time with either heelstrike (detected most likely by x-axis accelerometer), toe-off (detectedmost likely by y-axis gyro), or mid-stride during peak tibial angularvelocity. Sensor fusion is enabled and possible necessitated by “freerange” humans with autonomous data collection; not a clinical orcontrolled experimental environment.

A further example of using data generated by the sensor to provide somedirectional specificity is identifying inferior-superior instability,which will be apparent from data obtained by a x-axis accelerometer. Yetanother example is detecting rotational instability of the implant,which will be apparent from data generated by the x-axis gyroscope.

FIG. 38A illustrates representative cloud based systems and methods forgenerating and processing data, communication pathways, reportgeneration and revenue generation. FIG. 38B illustrates representativelocal based systems and methods for generating and processing data,communication pathways, report generation and revenue generation.

FIGS. 38A-B illustrate and summarize the roles of the intelligentprosthesis, base station, analytics, stored information, and variousexternal entities in providing automated and semi-automated services tothe patient. FIG. 38A illustrates a services-provisioning environmentthat includes cloud-resident data storage and analytics and FIG. 38Billustrates an alternative services-provisioning environment withoutcloud-resident data storage and analytics. In FIG. 38A, the reports,events, and alarms generated and distributed by the cloud-resident datastorage and analytics 2764, as discussed above with reference to FIG.37D, are output from cloud-resident data storage and analytics 2766 inresponse to receiving, and following processing, of monitoring-sessiondata and other data from the base station 2768 and additional datacollected from additional sources 2770. As also discussed above, themonitoring-session data is provided by the intelligent prosthesis 2772and/or additional patient-resident devices 2774 to the base station2768. The additional data 2770 may be cloud resident or may bealternatively requested from various types of on-line sources, includinginstitutional sources of healthcare records. The reports, events, andalarms 2764 are consumed by various different entities and individualsrepresented by block 2776. By contrast, in the services-provisioningenvironment shown in FIG. 38B, the reports, events, and alarms aregenerated by one or more of the various different entities andindividuals represented by block 2778 in cooperation with the basestation 2780, the intelligent prosthesis 2782, additionalpatient-resident devices 2784, and various additional types of data2786. In short, FIG. 38A illustrates a services-provisioning environmentin which cloud-resident storage and analytics plays a centralized rolein collecting information and generating reports, events, and alarms forconsumption by the various different entities and individualsrepresented by block 2776 while FIG. 38B illustrates an alternativeservices-provisioning environment in which reports, events, and alarmsare generated in a more distributed fashion by the various differententities and individuals represented by block 2778.

Either in the first services-provisioning environment shown in FIG. 38Aor the second services-provisioning environment shown in FIG. 38B, thereports, events, and/or alarms generated from analysis ofmonitoring-session data and other information, as shown in FIG. 38A, areconsumed by insurance companies 2788, medical practitioners 2790,medical facilities, including clinics and hospitals 2792, and, incertain cases, the patient 2794. Different types of reports, events,and/or alarms are generated for the different consuming entities andindividuals, depending on their information needs as well as onconfidentiality constraints, regulatory constraints, and otherconstraints. Each of the different types of reports may be generated atdifferent time intervals over different reporting time spans. As oneexample, monitoring-session data may be analyzed and aggregated togenerate progress reports furnished to a medical practitioner and/orclinical staff at a regular time interval over weeks to months followinginstallation of the prosthesis, allowing the medical practitioner tocarefully monitor a patient's progress during the critical, initialperiod of prosthesis use. Within certain embodiments the reports mayalso include recorded video or audio from a patient, as well assubjective data which may be collected from any of a number of sources.Subsequently, progress reports may be furnished less frequently.

As another example, a cumulative report of the distribution of progressreports to one or more medical practitioners may be furnished to one ormore insurance companies to allow automated generation of telemedicinecodes 2796 or other means for medical-practitioner reimbursement. Withinvarious embodiments, the services-provisioning environment can recordphysician and/or clinical staff review of the reports, and provideevidence of the same for reimbursement. Within preferred embodiments atleast 10, 15, 20, 30, 45, or 60 minutes of physician and/or clinicalstaff review would be recorded over the course of a month, and beprepared and submitted in a form suitable for reimbursement.

The various entities and individuals may cooperate to generate secondaryreports or requests that are automatically furnished to variousthird-party suppliers and service providers 2798. For example, theresponsible medical practitioner and/or clinical staff may determine,from a review of the aggregated monitoring-session reports, that thepatient needs additional equipment, pharmaceuticals, or other servicesand products, and may enter indications of these needs into the systemfor automated procurement of the additional equipment, pharmaceuticals,or other services and products on behalf of the patient. Within certainembodiments, the physician and/or clinical staff may require additionalequipment such as a knee brace, cane, walker, blood pressure monitoring,and/or a pharmaceutical product.

In further embodiments, automated procurement may involve patientinteraction with the equipment, pharmaceutical, and service providersnotified by these secondary requests and/or reports. Reports may bedistributed by a variety of different means, including email, audiorecordings, and textual and graphical information provided throughvarious types of electronic interfaces, including physician dashboardsand automated information services.

In addition to the reports, the system may generate various differenttypes of alarms and events, as discussed above with reference to FIG.37D. For example, the cloud-resident automated analytics module maydetect certain types of anomalies or problems that require immediateattention, and may generate alarms via the patient-resident devices2774, the base station 2768, or by telephone or electronic alerts to apatient's tablet or laptop, and may generate similar alarms forconsumption by medical practitioners, medical facilities and evenemergency medical-services providers. By contrast, events generated bythe cloud-resident automated analytics module may be used for concisereporting and notification to external automated systems used byinsurance companies, medical practitioners, and medical facilities. Asone example, the cloud-resident automated analytics module may, incertain implementations, generate events corresponding to monitoringsessions that are transmitted to a medical practitioner's dashboard sothat the medical practitioner is made aware of the fact that the patientis being successfully automatically monitored by the system. In manyimplementations, medical practitioners, medical facilities, andinsurance companies are provided tools for configuring the types ofalarms and events that they wish to receive and configuring the variousmethods for alarm and event transmission and notification of receivedalarms and events.

The services-provisioning environments shown in FIGS. 38A-B can beviewed, perhaps most generally, as a highly capable communicationssystem that supports data transmission and other communications betweena patient, an intelligent prosthesis within or on the patient, a varietyof different individuals and institutions, and many different electronicdevices and systems. As with all communication systems, theservices-provisioning environments shown in FIGS. 38A-B can be used formany different purposes, all of which may significantly contribute tohigh quality, timely, and objective-data-driven care for the patient. Byautomating communications and interactions, as well as prosthesis andpatient monitoring, the high-quality medical services are provided in afar more time-efficient and cost-effective manner than these servicescan be provided by individuals and institutions lacking the highlycapable automated communications system.

The following are exemplary embodiments of the present disclosure:

1. A monitoring-session-data collection, analysis, and status-reportingsystem implemented as a component of one or more computer systems, eachcomputer system having one or more processors, one or more memories, oneor more network connections, and access to one or more mass-storagedevices, the one or more the monitoring-session-data collection,data-analysis, and status-reporting system comprising:

-   -   a monitoring-session-data-receiving component that receives        monitoring-session-data, including acceleration data generated        by sensors within or proximal to a prosthesis attached or        implanted within a patient, from an external        monitoring-session-data source and that stores the received        monitoring-session-data in one or more of the one or more        memories and one or more mass-storage devices;    -   a monitoring-session-data-processing component that        -   prepares the monitoring-session-data for processing,        -   determines component trajectories representing motion modes            and additional metric values from the            monitoring-session-data; and    -   a monitoring-session-data-analysis component that        -   determines a prosthesis status and a patient status from the            motion modes and additional metric values,        -   distributes the determined prosthesis status and patient            status to target computer systems through the network            connections, and        -   when indicated by the determined prosthesis status and            patient status, distributes one or more alarms and events to            target computer systems through the network connections.

2. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 1 wherein themonitoring-session-data includes: a patient identifier; a deviceidentifier; a timestamp; device-configuration data; and an ordered setof data.

3. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 2 wherein the ordered set of datacomprises one of:

-   -   a time sequence of data vectors, each data vector including        numerical values related to linear-accelerations with respect to        three coordinate axes of an internal device coordinate system;        and    -   a time sequence of data vectors, each data vector including        numerical values related to linear-accelerations with respect to        three coordinate axes of a first internal device coordinate        system and including numerical values related to angular        velocities, numerical values related to angular velocities        relative to the first internal device coordinate system or to a        second internal device coordinate system.

4 The monitoring-session-data collection, analysis, and status-reportingsystem of embodiment 1 wherein the monitoring-session-data-processingcomponent prepares the monitoring-session-data for processing by:

-   -   receiving a time sequence of data vectors, each data vector        including three numerical values related to linear-accelerations        in the directions of three coordinate axes of a first internal        device coordinate system and including three numerical values        related to angular velocities about each axis of the first or a        second internal device coordinate system;    -   when rescaling of the data-vector sequence is needed, rescaling        the numerical values of the data vectors;    -   when normalization of the data-vector sequence is needed,        normalizing the numerical values of the data vectors;    -   when transformation of one or more of the numerical values        related to linear-acceleration and the numerical values related        to angular velocities is needed to relate the numerical values        related to linear-acceleration and the numerical values related        to angular velocities to a common internal coordinate system,        transforming one or more of the numerical values related to        linear-acceleration and the numerical values related to angular        velocities to relate to the common internal coordinate system;        and    -   when the time sequence of data vectors needs to be synchronized        with respect to a fixed-interval time sequence, synchronizing        the data vectors with respect to a fixed-interval time sequence.

5. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 1 wherein themonitoring-session-data-processing component determines componenttrajectories representing motion modes and additional metric values fromthe monitoring-session-data by:

-   -   orienting the prepared monitoring-session-data, comprising data        vectors, each data vector including three numerical values        related to linear-accelerations in the directions of three        coordinate axes of an internal device coordinate system and        including three numerical values related to angular velocities        about each axis of the internal device coordinate system, with        respect to a natural coordinate system;    -   bandpass filtering the oriented data vectors to obtain a set of        data vectors for each of multiple frequencies, including a        normal-motion frequency;    -   determining, from the data vectors for each of the        non-normal-motion frequencies, a spatial amplitude in each of        the coordinate-axis directions of the natural coordinate system;    -   determining, from a basis trajectory for the patient and the        data vectors for the normal-motion frequency, a spatial        amplitude in each of the coordinate-axis directions of the        natural coordinate system; and    -   determining, from the basis trajectory for the patient and the        data vectors for the normal-motion frequency, current        normal-motion characteristics.

6. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 5 wherein determining, from thedata vectors for a frequency, a spatial amplitude in each of thecoordinate-axis directions of the natural coordinate system furthercomprises:

-   -   generating a spatial trajectory from the data vectors;    -   projecting the spatial frequency onto each of the coordinate        axes; and    -   determining the lengths of the protections of the spatial        frequency onto each of the coordinate axes.

7. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 1 wherein themonitoring-session-data-analysis component determines a prosthesisstatus and a patient status from the motion modes and additional metricvalues by:

-   -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

8. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 1 wherein themonitoring-session-data-analysis component wherein the one or morealarms and events distributed to target computer systems include:

-   -   an alarm that notifies a medical practitioner or medical        facility of the need, by the patient, of immediate assistance or        intervention; and    -   an event that indicates additional services and/or equipment        needed by the patient that may be handled by various external        computer systems to automatically provide the additional        services and/or equipment to the patient or inform the patient        of the additional services and/or equipment and provide the        patient with information regarding procurement of the additional        services and/or equipment.

9. A method, carried out by a monitoring-session-data collection,analysis, and status-reporting system implemented as a component of oneor more computer systems, each computer system having one or moreprocessors, one or more memories, one or more network connections, andaccess to one or more mass-storage devices, the method comprising:

-   -   receiving monitoring-session-data, including acceleration data        generated by sensors within or proximal to a prosthesis attached        or implanted within a patient, from an external        monitoring-session-data source;    -   storing the received monitoring-session-data in one or more of        the one or more memories and one or more mass-storage devices;    -   determining a prosthesis status and a patient status from the        motion modes and additional metric values,    -   distributing the determined prosthesis status and patient status        to target computer systems through the network connections, and    -   when indicated by the determined prosthesis status and patient        status, distributing one or more alarms and events to target        computer systems through the network connections.

10. The method of embodiment 9 wherein determining a prosthesis statusand a patient status from the motion modes and additional metric valuesfurther comprises:

-   -   preparing the monitoring-session-data for processing,    -   determines component trajectories representing motion modes and        additional metric values from the monitoring-session-data;    -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

11. The method of embodiment 9 wherein preparing themonitoring-session-data for processing further comprises

-   -   receiving a time sequence of data vectors, each data vector        including three numerical values related to linear-accelerations        in the directions of three coordinate axes of a first internal        device coordinate system and including three numerical values        related to angular velocities about each axis of the first or a        second internal device coordinate system;    -   when rescaling of the data-vector sequence is needed, rescaling        the numerical values of the data vectors;    -   when normalization of the data-vector sequence is needed,        normalizing the numerical values of the data vectors;    -   when transformation of one or more of the numerical values        related to linear-acceleration and the numerical values related        to angular velocities is needed to relate the numerical values        related to linear-acceleration and the numerical values related        to angular velocities to a common internal coordinate system,        transforming one or more of the numerical values related to        linear-acceleration and the numerical values related to angular        velocities to relate to the common internal coordinate system;        and    -   when the time sequence of data vectors needs to be synchronized        with respect to a fixed-interval time sequence, synchronizing        the data vectors with respect to a fixed-interval time sequence.

12. The method of embodiment 9 wherein determining componenttrajectories representing motion modes and additional metric values fromthe monitoring-session-data by:

-   -   orienting the prepared monitoring-session-data, comprising data        vectors, each data vector including three numerical values        related to linear-accelerations in the directions of three        coordinate axes of an internal device coordinate system and        including three numerical values related to angular velocities        about each axis of the internal device coordinate system, with        respect to a natural coordinate system;    -   bandpass filtering the oriented data vectors to obtain a set of        data vectors for each of multiple frequencies, including a        normal-motion frequency;    -   determining, from the data vectors for each of the        non-normal-motion frequencies, a spatial amplitude in each of        the coordinate-axis directions of the natural coordinate system;    -   determining, from a basis trajectory for the patient and the        data vectors for the normal-motion frequency, a spatial        amplitude in each of the coordinate-axis directions of the        natural coordinate system; and    -   determining, from the basis trajectory for the patient and the        data vectors for the normal-motion frequency, current        normal-motion characteristics.

13. The method of embodiment 9 wherein determining, from the datavectors for a frequency, a spatial amplitude in each of thecoordinate-axis directions of the natural coordinate system furthercomprises:

-   -   generating a spatial trajectory from the data vectors;    -   projecting the spatial frequency onto each of the coordinate        axes; and    -   determining the lengths of the protections of the spatial        frequency onto each of the coordinate axes.

14. The method of embodiment 9 wherein determining a prosthesis statusand a patient status from the motion modes and additional metric valuesfurther comprises:

-   -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

15. The method of embodiment 9 wherein the one or more alarms and eventsdistributed to target computer systems include:

-   -   an alarm that notifies a medical practitioner or medical        facility of the need, by the patient, of immediate assistance or        intervention; and    -   an event that indicates additional services and/or equipment        needed by the patient that may be handled by various external        computer systems to automatically provide the additional        services and/or equipment to the patient or inform the patient        of the additional services and/or equipment and provide the        patient with information regarding procurement of the additional        services and/or equipment.

16. A physical data-storage device encoded with computer instructionsthat, when executed by one or more processors within one or morecomputer systems of a monitoring-session-data collection, analysis, andstatus-reporting system, each computer system having one or moreprocessors, one or more memories, one or more network connections, andaccess to one or more mass-storage devices, control themonitoring-session-data collection, analysis, and status-reportingsystem to:

-   -   receive monitoring-session-data, including acceleration data        generated by sensors within or proximal to a prosthesis attached        or implanted within a patient, from an external        monitoring-session-data source.

In each of the foregoing exemplary embodiments of the presentdisclosure, the present disclosure also provides exemplary embodimentswherein, in the computer system(s), the monitoringsession-data-processing component determines component trajectoriesrepresenting motion modes and not necessarily also representingadditional metric values, from the monitoring-session data. In otherwords, determining component trajectories representing the additionalmetric values is optionally performed. Likewise, in the methods carriedout by a monitoring-session-data collection in embodiments of thepresent disclosure, the present disclosure also provides exemplaryembodiments wherein determining a prosthesis status and/or a patientstatus is accomplished from the motion mode, and not necessarily alsofrom the additional metric values. In other words, determining aprosthesis status and/or a patient status is optionally done from theadditional metric values. Also, in the methods carried out by amonitoring-session-data collection in embodiments of the presentdisclosure, the present disclosure also provides exemplary embodimentswherein the method may also include determining component trajectoriesrepresenting motion modes, and optionally additional metric values, fromthe monitoring-session-data, to thereby provide the motion modes and/orthe additional metric values from which may be determined a prosthesisstatus and/or a patient status as recited in the methods of theexemplary embodiments.

E. Methods and Devices for Stabilizing an Artificial Joint

Total joint arthroplasty (TJA) prosthetic devices are available forreplacement of multiple joints in the human body, such as total kneearthroplasty (TKA), total hip arthroplasty (THA), total shoulderarthroplasty (TSA), ankle arthroplasty and elbow arthroplasty. While thedesign differs by anatomical location and the specific needs of thepatient, typically both articular surfaces of the diseased joint aresurgically removed (although in some instances only one joint surface iscompletely, or partially, removed), one articular surface is replaced bya polished metallic prosthesis anchored directly into one adjacent longbone, and the opposing articular surface is replaced by a polymeric“spacer” supported by a metallic prosthesis anchored into the otheradjacent long bone marrow cavity. While different metal alloys,polymeric formulations and even ceramic or other materials may be usedin various combinations, all TJA devices follow the same basic designprinciples. The intelligent implant technology described in the presentembodiment can be contained in any of the components of a TJA, includingthe metallic prostheses on one or both sides of the joint and thepolymeric spacer located in between them. Particularly preferredlocations to incorporate the intelligent implant technology include: thetibial stem and the tibial stem extension of a total knee arthroplasty(TKA), the femoral stem of a total hip arthroplasty (THA), the humeralstem of a total shoulder arthroplasty (TSA), the humeral component oftotal elbow arthroplasty, and the tibial component of a total anklearthroplasty.

By way of specific illustration, the typical nomenclature to describesystems for a total knee arthroplasty (TKA) is provided herein withreference to FIG. 39 and FIGS. 40A to 40D. In FIG. 39, a system (3010)for TKA can consist of up to five components: a femoral component(3012), a tibial insert (3014), a tibial component (3016), a tibialextension (not shown in FIG. 39; shown in FIG. 40) and a patellacomponent which is positioned in front of the joint (also omitted fromFIG. 39 as well as FIG. 40 for the sake of clarity). The components aredesigned to work together as a functional unit. The tibial insert (3014)is sometimes called a spacer or an articulating surface. The tibialcomponent (3016) includes a base plate (3018), which is sometimes calleda tibial plate, a tibial tray, or a tibial base plate, and a segmentthat inserts into the marrow cavity of the tibia called the tibial stem(3020). The superior surface of the base plate (3018) contacts andsupports the tibial insert (3014). The tibial component may also includea tibial stem extension (not shown in FIG. 39) that attaches to thetibial stem (3020) at its distal end. As shown in FIG. 40, the tibialstem (3020) may be hollow and may terminated with a “female” opening(3022), where this opening/hollow cavity may be positioned to receive a“male” portion of the tibial stem extension (3025) in order to assist inseating the tibial stem extension (3023) into the tibial stem (3020).There are numerous methods of securing the coupling between the tibialstem (3020) and the tibial stem extension (3023) including threading(screwing attachment), specific complimentary coupling attachments andlocking screws. While the male coupling part of the tibial stemextension (3025) is contained within the female portion of the tibialstem, the external portion of the tibial stem extension (3024)effectively lengthens the stem portion of the TKA when surgically placedinto the tibial marrow cavity and serves to better stabilize the TKAprosthesis.

FIG. 40A and FIG. 40B provide two different perspective images of atibial component (3016), each comprising of a base plate (3018) and atibial base plate stem (3020). FIG. 40C provides a perspective image ofa tibial stem extension (3023). As described above, the tibial stemextension consists of a segment (3025) that inserts into the tibial basestem and a portion which protrudes (3024) to lengthen the overall tibialstem portion of the TKA. FIG. 40D shows the assembled configuration(3026) with the stemmed tibial plate now composed of sections from thebase plate stem (3020) and the distal portion of the tibial extension(3024). As described previously, the intelligent implant can be locatedin the femoral component, the tibial component and/or the spacer,however preferred locations include the tibial stem and the tibial stemextension (as shown in FIGS. 1 and 2).

In some instances, the TJA (e.g. TKA, THA and TSA) may not be stably oranatomically correctly implanted into the patient. It may, for example,demonstrate some degree of misalignment and/or movement relative to theimplanted bones and/or the polymeric articular surface, e.g., somedegree of wiggle or wobble. This instability or malalignment is, ofcourse, undesirable and can lead to pain, gait/movement problems,physical limitations and patient dysfunction. Poor alignment orinstability in the TJA hardware may also lead to bone erosion andaccelerated fatigue of the implant components. Left untreated oruncorrected, bone erosion and accelerated fatigue will typically lead toboth pain and inflammation. By the time pain and inflammation prompt aTJA patient to seek medical care, the extent of bone erosion and TJAfatigue may leave the health care professional with only one-choice: ahighly invasive and expensive surgery with reduced probability of“successful” outcome.

Currently, early identification of subclinical issues is eitherdifficult or impossible since they are often too subtle to be detectedon physical exam or demonstratable by radiographic studies. Even ifdetection were possible, corrective actions would be hampered by thefact that the specific amount movement and/or degree of improperalignment cannot be accurately measured or quantified, making targeted,successful intervention unlikely. The present disclosure providesintelligent implants, devices, systems and methods which provide thatthe misalignment and/or instability in the TJA hardware can be detectedearly, before bone erosion and implant fatigue damage has progressed tosignificant levels. Once misalignment or instability is detected andcharacterized, the results can be communicated to a health care providerto allow for early treatment and/or more effective treatment of theproblem, i.e., the health care provider may take advantage of correctivetreatments that are far less invasive, less expensive, and more likelyto succeed. The embodiments of the present disclosure address many ofthe above problems by, (1) identifying the presence of improperalignment and instability through monitoring the patient's dailyactivity and function via an intelligent implant collecting data under“real world” physical conditions post-operatively, (2) quantifying thespecific degree and amount of misalignment or instability identified bysuch monitoring, (3) using the intelligent implant to identify anynegative changes or progression (or positive changes in the event ofeffective treatment) that occur over time, that therefore allow (4) thedesign and implementation of specific, pre-emptive, corrective,minimally-invasive measures to address the problems identified. Thepresent disclosure also provides devices and/or methods to addressand/or treat the instability and/or misalignment problem. Correctingabnormalities early, prior to the development of significant bone loss,will not only improve the patient's symptoms (pain, impairedambulation), but also prolong the effective lifespan of the TJAprocedure and reduce the need repeat and highly invasive, surgical,corrective procedures.

In one aspect, the present disclosure provides for obtaining data fromintelligent implants that documents the degree of misalignment, theanatomic location of the loosening and/or the absolute degree ofloosening of the implanted TJA. This precise, site-specific datacollected directly from the patient's intelligent TJA implant and thecorresponding analyzed data may be used by the health care professionalto determine whether, what type, and when an intervention is desired.With minor deficiencies, corrective external bracing, using commerciallyavailable joint braces, can be used to restore proper alignment andprovide enhanced stability. Determining the correct degree ofmisalignment and/or the magnitude of instability via the intelligent TJAallows the design of a corrective brace specifically tailored to thepatient's deficiency. For lower limb TJA (TKA, THA and anklearthroplasty), customized orthotics can be utilized for the samepurposes. For more severe or advanced misalignment or instability,minimally-invasive corrective measures can be employed. The health careprovider may recommend an appropriate intervention to address theinstability depending upon whether the TJA component has been cementedinto place or is closely fitted into the bone without the use of cement.For example, when the TJA is an uncemented intelligent prosthesis (suchas an uncemented TKA or an uncemented THA), and misalignment and/orinstability of the TJA is causing pain for the patient, data obtainedfrom the intelligent device can be used to monitor and locate theanatomical location and amount/degree of misalignment or instability.Localized and precise amounts of bone cement can then beinjected/applied minimally-invasively to the specific area to correctthe abnormality. The patient is monitored by the intelligent TJAperi-procedurally to confirm successful correction and post-procedurallyto follow the patient's functional response to treatment. For cementedTJA patients, monitoring data may be used to determine whether theinterface between the bone cement and the prosthesis has broken. As withthe uncemented TJA, bone cement can be delivered to the correct locationin the required amount, using techniques and devices described ingreater detail below, to correct the broken cement-bone interface.

In the event that a determination is made that the instability is due toinsufficient osteointegration, the present disclosure provides a methodfor enhancing osteointegration in order to address the TJA instability.Thus, the present disclosure provides methods that include detecting thepresence of TJA instability using the intelligent TJA, optionallylocating, measuring or otherwise characterizing the TJA instability, andperforming a minimally-invasive intervention in order to improve theprocess of osteointegration at the required sites surrounding the TJA.Such intervention may include using autologous bone graftplacement/injection, xenograph bone graft placement/injection, syntheticbone graft placement/injection, bone pastes, injection of bone growthfactors (such as Bone Morphogenic Protein or BMP), injection of othergrowth factors, and methods for locating such therapeutic agents at thesite of the TJA installation.

In one aspect, the intelligent implant has generated data indicatingthat only one or more isolated areas around the TJA is/are loose.Accordingly, the present disclosure provides a method where that/thosespecific area(s) is/are identified, and autologous, xenographic orsynthetic bone graft material (with or without growth factors such asBMP), and/or bone cement or other fixation means is/are directed solelyto that/those area(s). In effect, this provides a spot welding approachto addressing the source(s) of instability.

In the event the TJA component is not cemented into place (for example,an uncemented TKA or THA), the present disclosure provides tools thatmay be used to inject/place autologous, xenographic or synthetic bonegraft material (with or without growth factors such as BMP), and/orcement to improve stability. For example, a fenestrated screw or otherhollow, boring device may be inserted into a bone, terminating in thespace between the uncemented component and the nearby inner surface ofthe bone. The access thus provided can be used to inject/placeautologous, xenographic or synthetic bone graft material (with orwithout growth factors such as BMP), and/or cement into the spacesurrounding the uncemented TJA. Cement and/or xenographic or syntheticbone graft material (with or without growth factors such as BMP) may bedeposited into this space, in order to fill the space and to secure thetibial component into a permanent position, i.e., a non-moving positionwith respect to the surround bone.

In one aspect, the present disclosure provides a method that includesdetecting the presence of a TJA instability, optionally characterizingthat instability, and then intervening in order to stabilize the TJA. Inone aspect, the intervention is a method including drilling a holethrough the bone cortex surrounding the TJA, and injecting/placingautologous, xenographic or synthetic bone graft material (with orwithout growth factors such as BMP), and/or cement into a space betweenthe TJA and the surrounding bone.

Optionally, the space between the TJA and the surrounding bone may notbe sufficiently large to receive the amount of cement that is desirablyinjected. In this case, the present disclosure provides a method thatinclude inserting a balloon into the space between the TJA and thesurrounding tibia bone, and inflating that balloon to open up additionalspace between the TJA and the surrounding bone, and theninjecting/placing autologous, xenographic or synthetic bone graftmaterial (with or without growth factors such as BMP), and/or cementinto the opened space.

In another aspect, the present disclosure provides a method thatincludes detecting the presence of improper alignment of the TJAprosthetic component using the intelligent implant, optionallycharacterizing that axis and the degree of misalignment, and thenintervening in order to correctly align the prosthetic component. In oneaspect, the intervention is a method including drilling a hole throughthe bone cortex surrounding the TJA, and injecting/placing autologous,xenographic or synthetic bone graft material (with or without growthfactors such as BMP), and/or cement into a specified space, in aspecified amount, between the TJA and the surrounding tibia bone so to“push” the stem of the TJA in the correct axis to correct themisalignment. The present disclosure also provides a method thatincludes inserting a balloon into the space between the TJA and thesurrounding bone and inflating that balloon to “push” the prostheticjoint stem in the correct axis to correct the misalignment, and theninjecting cement into the opened space. The present disclosure alsoincludes the use of a screw, wire, rod or other physical device insertedinto the space between the TJA and the surrounding bone to “push” theprosthetic stem in the correct axis, and the correct amount/distance, inorder to correct the misalignment, and then injecting/placingautologous, xenographic or synthetic bone graft material (with orwithout growth factors such as BMP), and/or cement into the opened spaceto permanently realign the TJA. In the event that osteointegration hasalready occurred when the misalignment or instability has been detectedor is going to be addressed, then the above devices and methods can beused to push the prosthetic stem portion of the device into the correctalignment where it can be permanently embedded correctly through thesubsequent application of bone cement, autologous, xenographic orsynthetic bone graft material (with or without growth factors such asBMP), or other fixation technique.

Suitable tools and balloons for stabilizing or realigning a TKIaccording to the present disclosure may be the same or analogous to thetools and/or balloons that are suitable for use in intervertebral disctherapies such as kyphoplasty procedures.

Optionally, in one embodiment, the intelligent implant is interrogatedduring the procedure used to address the misalignment and/orinstability, and based on information obtained from the intelligentimplant, the surgeon can optimize the amount and positioning of thecement, xenographic or synthetic bone graft material (with or withoutgrowth factors such as BM P) being used to stabilize the implant.

In the event that a determination is made that the instability is due tothe presence of a prosthetic joint infection (PJI), the presentdisclosure provides a method for treating the PJI in order to addressthe TJA instability. Prosthetic joint infection is a seriouscomplication that occurs in approximately 1% of TJA patients and isfrequently caused by skin bacteria (often Staphylococcus epidermidis orStaphylococcus aureus). It can be difficult to diagnose and treat due tothe indolent nature of the infection and due to the lack of specificsigns and symptoms and is therefore often quite advanced by the time itpresents clinically. In many cases it can result in failure of the TJAprocedure and the need to remove the infected implant. The presentdisclosure provides methods that include early detection of the presenceof PJI using the intelligent TJA thus allowing the healthcareprofessional to confirm the presence of PJI, determine its location andextent, and perform systemic and/or local therapeutic interventions inorder to treat and/or eradicate the infection. Such interventions mayinclude the administration of systemic antibiotics, using localizedirrigation of the marrow cavity with antibiotics, the local applicationof sustained release antibiotic preparations at the site of theinfection, the local application of other therapeutic agents, andmethods for locating such therapeutic agents at the site of the TJAinfection using the devices and techniques described above.

F. Methods and Devices for Adjusting Position of an Artificial Joint

In some instances, the TJA may be stably implanted into the patient,however, the positioning of the TJA is suboptimal. This can occur evenafter a “successful” implant if the patient loses a great deal of weightor has another joint replaced. Furthermore “perfect” anatomicalalignment for a particular patient may not be “ideal” for them as theirinherent anatomy may be a few degrees deviated from the normal position.This suboptimal placement of the TJA prosthesis, e.g., TKI, may lead toproblems, including pain, distorted gait, difficulty performing certainactivities (e.g. climbing stairs, getting out of chairs), weakness,instability and even accelerated wear in all or parts of the TJAprosthesis. By way of illustration, incorrect placement is thought to bea significant contributor to the 20% of patients who reportdissatisfaction with their TKA surgery. Data collected from theintelligent TJA prosthesis of the present disclosure can be used todetect problems with placement, alignment and functioning of the TJAprosthesis and be utilized by the healthcare professional to takecorrective actions to alleviate symptoms and prevent longer-termsequalae.

In one aspect, the present disclosure provides a series of tamps thatcan finely adjust the positioning of the TJA within the patient,particularly if the non-optimal positioning is detected early by theintelligent TJA, before substantial osteointegration has taken place.The present disclosure also provides a method of adjusting the positionof an implanted TJA to an improved position, which includes using a tampto adjust the position of the TJA.

In one aspect, the present disclosure provides metal pins and/or K-wiresthat can be used to adjust the positioning of the TJA within thepatient, particularly if the non-optimal positioning is detected early,before substantial osteointegration has taken place. The presentdisclosure also provides a method of adjusting the position of animplanted TJA to an improved position, which includes using a metal pinor K-wire to adjust the position of the TJA to a position morebeneficial to the patient.

In one aspect, the present disclosure provides metal pins and/or K-wiresthat can be used to adjust the positioning of the TJA within thepatient, particularly if the non-optimal positioning is detected earlyby the intelligent TJA, before substantial osteointegration has takenplace. The present disclosure also provides a method of adjusting theposition of an implanted TJA to an improved position, which includesusing a metal pin or K-wire to adjust the position of the TJA.

In one aspect, the present disclosure provides a “Christmas tree stand”approach, where two, three or more (e.g., 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20) screws are inserted into the bone atdifferent locations (e.g. for three screws, at 0°, 120° and 240°) so asto apply pressure to the shaft of the TJA prosthetic stem (or stemextension if present) and adjust/push the TJA into the desiredanatomical location. This approach can be used to adjust the positioningof the TJA within the patient, particularly if the non-optimalpositioning is detected early, e.g., before clinical symptoms arise, andtypically before substantial osteointegration has taken place. Thepresent disclosure also provides a method of adjusting the position ofan implanted TJA to an improved position, which includes using this“Christmas tree stand” approach to adjust the position of the TJA. Inthis approach, a hole is drilled between the outer surface of the boneand the inner surface adjacent to the implant. A screw is then insertedthrough this hole, until the end of the screw touches the implant. Thescrew is further inserted, pushing against the implant and causing theimplant to tilt slightly. In this way, the position of the implantwithin the bone is adjusted to the desired location. Additional screwsmay be used to move the implant, and/or to hold the implant in a desiredlocation.

G. Joint Inserts and Use Thereof

As described previously, the TJA may not be stably or anatomicallycorrectly implanted into the patient due to some degree of misalignmentand/or movement relative to the stem component, the polymeric insert orthe other articular component. Abnormal movement of the TJA componentsin contact with the polymeric insert (or the other articular surface)can lead to abnormal wear of the polymer surface, accelerated fatigue ofthe articular polymers, the generation and liberation of microscopicpolymeric fragments into the joint space (which can in turn cause painand inflammation) and even early failure of the TJA implant itself.Instability, abnormal motion/movement and/or misalignment with respectto the TJA articular surface (i.e. the polymeric insert and the opposingarticular surface) clinically manifests itself as pain, inflammation,gait/movement problems, joint instability, joint subluxation, physicallimitations and patient dysfunction. By the time pain and inflammationprompt a TJA patient to seek medical care, expensive and invasivereplacement surgery may be required.

Currently, early identification of subclinical TJA articular surfaceissues are either difficult or impossible since they are often toosubtle to be detected on physical exam or demonstratable by radiographicstudies. Even if detection were possible, corrective actions would behampered by the fact that the specific amount movement, subluxation,degree of improper alignment, or irregular and accelerated wear cannotbe accurately measured or quantified, making targeted, successfulintervention unlikely. The present disclosure provides intelligentimplants, devices, systems and methods which provide that themisalignment and/or improper movement with respect to the TJA articularsurface can be detected early, before polymer erosion has led topermanent problems with the TJA prosthesis. Once misalignment, impropermovement, and/or instability is detected and characterized by anintelligent TJA implant, the results can be communicated to a healthcare provider to allow for early treatment and/or more effectivetreatment of the problem, i.e., the health care provider may takeadvantage of corrective treatments that are far less invasive, lessexpensive, and more likely to succeed. Embodiments of the presentdisclosure address many of the above problems in several ways, butinclude non-invasive interventions such as external bracing andorthotics (for lower limb prostheses), as well as less invasive surgicalinterventions such as the removal of a failing polymeric insert andreplacing it with a customized polymeric insert designed to correct theidentified problems. Correcting abnormalities early, prior to thedevelopment of TJA failure, will not only improve the patient's symptoms(pain, inflammation, impaired joint motion, impaired ambulation), butalso prolong the effective lifespan of the TJA procedure and reduce theneed for repeat and highly invasive corrective procedures.

Polymeric inserts are used in many TJA applications. In embodiments, thepresent disclosure provides asymmetrical polymeric inserts for a hipimplant, a knee implant, a shoulder implant, an elbow implant and a kneeimplant. The polymeric insert may be a spacer, an articular spacer or anintraarticular spacer. In one embodiment the spacer is a static spacer.In one embodiment the spacer is an articulating spacer.

FIG. 41 provides a specific example of a tibial insert (3014) used in aTKA procedure. In FIG. 41, the tibial insert (3014) is shown to have anaxis (3040) that cuts across the longest width (medial-lateralanatomically) of the insert (3014), and extends from the medial edge ofthe insert at point 3040 a to the lateral edge of the insert at point3040 b, and crossing a center line of the insert at point at 3040 c,where point 3040 c is mid-way between points 3040 a and 3040 b. Inaddition, the tibial insert (3014) has an axis (3042) that cuts acrossthe depth (anterior-posterior anatomically) of the insert (3014). Takentogether, these two axes (3040) and (3042) divide the tibial insert intofour quadrants, when the insert (3014) is viewed from its superiorsurface (3052), or the surface that contacts the femoral component(3012). The medial-lateral axis (3040) divides the insert (3014) into ananterior side (3044) and a posterior side (3046). The anterior-posterioraxis (3042) divides the insert (3014) into a medial side (3048) whichwill be closest to the recipient's second knee (i.e. the midline), and alateral side (3046) which will be on the outside of the knee (away fromthe recipient's second knee; for further clarity, the insert (3014)depicted in FIG. 41 is one located in the left knee of a TKA recipient).As also shown in FIG. 41, the anterior side (3044) of the insert (3014)may have a recess (3054) to accept the patella component (not shown),while the posterior side (3046) often features a “notch” that mimics theanatomy of the tibia. The insert (3014) may also have a sidewall (3056)that extends around the insert (3014).

FIG. 42A illustrates a cross-section (superior-inferior anatomically) ofthe tibial insert (3014) of FIG. 41, as viewed along the axis 3040. Inthe cross-sectional view of FIG. 42A, the insert (3014) is shown to havea top (superior) articular surface (3052) and a bottom (inferior)surface (3058), where the inferior surface (3058) would typically beheld in place by the tibial plate (3016) and the superior surface (3052)would be in contact with the femoral head. Also shown in FIG. 42A arethe medial edge (3080) which would be at point 3040 a in FIG. 41, andthe lateral edge (3084) which would be at point 3040 b of the insert(3014) also shown in FIG. 41, where the medial edge (3080) has a height(3082) (also referred to as a thickness (3082)) extending from thebottom (inferior) surface (3058) to the top (superior) surface (3052) ofthe implant, and likewise the lateral edge (3084) has a height (3086)extending from the bottom (inferior) surface (3058) to the top(superior) surface (3052) of the implant. In addition, the insert (3014)has a height (3088) at the midpoint of the insert, i.e., at point 3040 cas identified in FIG. 41. In addition, the insert (3014) will have aheight (3090) which is the shortest height on the medial side of theinsert (3014), and will have a height (3092) which is the shortestheight on the lateral side of the insert (3014).

In one aspect, the present disclosure provides a tibial insert having ashortest height (3090) on the medial side of the insert which is notequal to the shortest height (3092) on the lateral side of the insert.In one aspect, the present disclosure provides a tibial insert having ashortest height (3090) on the medial side of the insert which is lessthan the shortest height (3092) on the lateral side of the insert. Inone aspect, the present disclosure provides a tibial insert having ashortest height (3090) on the medial side of the insert which is greaterthan the shortest height (3092) on the lateral side of the insert. Theterm least thickness may be used in lieu of shortest height.

For example, in embodiments, the present disclosure provides a tibialinsert for an implantable TKA prosthesis, where the tibial insert isabout 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker (higher) on themedial side of the implant, as compared to the lateral side. Such anembodiment is illustrated in FIG. 42B, where the thickness (3090) on themedial side of the insert (14) is shown to be greater than the thickness(3092) on the lateral side of the insert (3014). This configurationcould be desirable, for example, in a patient experience medial kneeinstability where the prosthesis exhibits some degree of undesirablemovement or subluxation towards the midline. After using the intelligentimplant to determine the anatomical location, direction andamount/degree of movement leading to the medial instability, acustomized tibial insert could be created with a higher medial minimumthickness and a lower lateral minimum thickness to correct the abnormalmovement and instability. Thus, rather than replacing the entire joint,the knee could be opened surgically, the ineffective tibial insertremoved, and replaced with a superior (for that patient), customizedtibial insert. Early detection by the implanted sensors would allowcorrection prior to significant bone loss or implant damage requiringhighly invasive TKA revision surgery.

As another example, in embodiments, the present disclosure provides atibial insert for an implantable TKA prosthesis, where the tibial insertis about 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker (higher) on thelateral side of the implant, as compared to the medial side. This designwould be used as described above, but in patients experiencing abnormallateral movement and instability.

FIG. 43 provides an additional perspective illustration of a tibialinsert (3014). In FIG. 43, the tibial insert (3014) is shown to have twoaxes (3100) and (3102) that cut across the thinnest portion of themedial and lateral sides, respectively, of the insert (3014), eachrunning in an anterior-posterior direction. Axis 3100 may be furthercharacterized as having a point 3100 a located at the anterior edge ofthe insert (3104), a point 3100 b located at the posterior edge of theinsert (3104) and a center point (mid-point) located at position 3100 cwhich is mid-way between points 3100 a and 3100 b.

FIG. 44A shows a cross-section of the insert (3014) of FIG. 43, asviewed along the line 3100. Thus, in FIG. 44A, the insert (3014) has ananterior edge (3112) located at position 3100 a in FIG. 43, having aheight (also known as thickness) (3110) and a posterior edge (3116)located as position 3100 b in FIG. 43, having a height (3114) where aheight is measured as the distance of the edge 3110 or 3114 extendingfrom the top surface (3052) to the bottom surface (3058) of the insert(3014). In addition, the insert (3014) has a height (3118) at a centerpoint located at the intersection of axes (3040) and (3100), i.e.,position 3100 c in FIG. 43. On either side off this centerpoint, theinsert will have an average height (3120) on the anterior side, and anaverage height (3122) on the posterior side, respectively, of the insert(3014). The average height 3120 is 50% of the combined heights 3100 and3118, while the average height 3122 is 50% of the combined heights 3114and 3118.

In one aspect, the present disclosure provides a tibial insert having anaverage height (3120) on the anterior side of the insert which is notequal to the average height (3122) on the posterior side of the insert.In one aspect, the present disclosure provides a tibial insert having anaverage height (3120) on the anterior side of the insert which is lessthan the average height (3122) on the posterior side of the insert. Inone aspect, the present disclosure provides a tibial insert having anaverage height (3120) on the medial side of the insert which is greaterthan the average height (3122) on the lateral side of the insert. Theterm least thickness may be used in lieu of height.

For example, in embodiments, the present disclosure provides a tibialinsert for an implantable knee prosthesis, where the medial side of thetibial insert has an average thickness (3120) on its anterior portionwhich is about 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm greater than theaverage thickness (3122) on the posterior portion of the medial side.Such an embodiment is illustrated in FIG. 44B, where the thickness(3120) on the anterior side of the insert (14) is shown to be greaterthan the thickness (3122) on the posterior side of the insert (3014).This design would be used as described previously, but in patientsexperiencing abnormal anterior movement and instability.

As another example, in embodiments, the present disclosure provides atibial insert for an implantable knee prosthesis, where the medial sideof the tibial insert has an average thickness (3120) on its anteriorportion which is about 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm less thanthe average thickness (3122) on the posterior portion of the medialside. This design would be used as described previously, but in patientsexperiencing abnormal posterior movement and instability.

As another example, in embodiments, the present disclosure provides atibial insert for an implantable knee prosthesis, where the lateral sideof the tibial insert has an average thickness (3120) on its anteriorportion which is about 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm greater thanthe average thickness (3122) on the posterior portion of the lateralside of the implant.

As yet another example, in embodiments, the present disclosure providesa tibial insert for an implantable knee prosthesis, where the lateralside of the tibial insert has an average thickness (3120) on itsanterior portion which is about 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mmless than the average thickness (3122) on the posterior portion of thelateral side of the implant.

In another embodiment, the present disclosure provide a tibial insertfor an implantable knee prosthesis, and a TKA system comprising a tibialinsert, where the tibial insert is about 1, 2, 3, 4, 5, 6, 7, 8, 9, or,10 mm thicker on the medial, lateral, anterior and/or posterior side ofthe implant. Thus, the present disclosure provides that each of the fourquadrants of a tibial inset, i.e., the anterior and posterior portionsof the medial side of the implant, and the anterior and posteriorportions of the lateral side of the implant, may have a unique height.Abnormal movement, instability and subluxation of the TKA joint canoccur in any direction and the embodiments described above can becombined to design a tibial insert capable of correcting the undesirablemovement.

In one embodiment, the present disclosure provides a tibial insert thathas been customized to the particular needs of a patient. These needsmay be determined using the implanted sensors and associated analysisprovided herein. Also, the tibial insert of the present disclosure maybe formed into shapes other than that shown in FIG. 39. For example, asshown in FIG. 45, the tibial insert 3140 of the present disclosure mayhave a horn 3142 that can extend into a femoral component, where thetibial insert 3140 will also have a space 3054 to fit a patella implant,a top surface 3052 which articulates with the femoral component, and aside 3056. Alternatively, or additionally as illustrated in FIG. 46, thetibial insert 3150 may include a spike 3152 that may extend into atibial component 3016, where the insert 3150 as shown in FIG. 46 alsohas a top surface 3052, a space 3054 for a patella implant, a sidesurface 3056 and a hole 3142.

While the descriptions and examples provided above are specific to TKAembodiments, as described previously, many other total jointarthroplasty (TJA) products also feature a polymeric insert. While theexact anatomy will differ for hip (THA), shoulder (TSA), elbowarthroplasty and ankle arthroplasty, the same principles apply: (1) theintelligent implant is utilized to identify the location, direction andextent of the abnormal movement, instability or subluxation, (2) acustomized polymeric insert, which may also be referred to as a spaceror an articular spacer or intraarticular spacer, can be designed andcreated to minimize, resist and/or eliminate the observed direction ofabnormal movement, instability or subluxation (3) the existing,ineffective polymeric insert is then surgically removed, and (4) thecustomized polymeric insert is implanted in its place to reduces, resistor eliminates the undesirable movement, instability and/or subluxation.Instituted early enough, these embodiments can prevent the developmentof irreparable damage to the TJA or the surrounding bone tissue toprolong the effective lifespan of the TJA and reduce the need forinvasive, expensive, revision surgery.

The TJA polymeric insert according to the present disclosure may be madeby any suitable technique. Exemplary techniques include 3-D printing,also known as additive manufacturing, or by molding, or by machiningsuch as computer numerical control (CNC) machining.

The TJA polymeric insert of the present disclosure may be composed ofany suitable material. Exemplary suitable materials include polyethylenesuch as high molecular weight polyethylene and ultra-high molecularweight polyethylene, or polyether ether ketone (PEEK).

The following are exemplary numbered embodiments of the presentdisclosure.

1. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe medial side of the implant, as compared to the lateral side.

2. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe lateral side of the implant, as compared to the medial side.

3. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe anterior side of the implant, as compared to the posterior side.

4. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe posterior side of the implant, as compared to the anterior side.

5. A tibial insert/articular spacer/for an implantable knee prosthesis,comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mmthicker on the medial, lateral, anterior and/or posterior side of theimplant.

6. The tibial insert according to any one of embodiments 1-5, whereinsaid tibial insert is composed of polyethylene, or polyetheretherketone(PEEK).

7. The tibial insert according to any one of embodiments 1-6 whereinsaid tibial insert is customized to a patient.

8. The tibial insert according to any one of embodiments 1 to 7 whereinsaid insert is manufactured by 3-D printing, or, by molding.

When the tibial insert is described as having a thickness on a medial,lateral, anterior and/or posterior side of the implant, this descriptionis made in reference to when the insert is positioned adjacent to theimplant within the patient, where the medial, lateral, anterior and/orposterior sides of the implant corresponds to the medial, lateral,anterior and/or posterior sides, respectively, of the insert that sitsadjacent to the implant.

The following are exemplary numbered embodiments of the presentdisclosure.

9. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1-10 mm thicker, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9,or, 10 mm thicker on a medial side of the insert, as compared to alateral side of the insert.

10. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1-10 mm thicker, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9,or, 10 mm thicker on a lateral side of the insert, as compared to amedial side of the insert.

11. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1-10 mm thicker, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9,or, 10 mm thicker on an anterior side of the insert, as compared to aposterior side of the insert.

12. A tibial insert for an implantable knee prosthesis, comprising atibial insert that is 1-10 mm thicker, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9,or, 10 mm thicker on a posterior side of the insert, as compared to ananterior side of the insert.

13. A tibial insert or articular spacer for an implantable kneeprosthesis, comprising a tibial insert that is 1-10 mm thicker, e.g., 1,2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker on one of a medial, lateral,anterior and/or posterior side of the insert compared to a correspondingside of the insert, where medial and lateral are corresponding sides andanterior and posterior are corresponding sides.

14. The tibial insert according to any one of embodiments 9-13, whereinsaid tibial insert is composed of polyethylene, or polyetheretherketone(PEEK).

15. The tibial insert according to any one of embodiments 9-14 whereinsaid tibial insert is customized to a patient.

16. The tibial insert according to any one of embodiments 9-15 whereinsaid insert is manufactured by 3-D printing, or, by molding.

H. Clinical Solutions and Products

Through signal processing techniques of accelerometer and gyroscopicsensors, the absolute length of motion associated with core lower limbgait or upper limb movement, macroscopic instability, and microscopicinstability can be calculated. In some instances, the ability to resolvedifferences and/or the presence of these abnormal motions is enhanced bylooking at an individual's kinematic motion relative to (1) a populationof other individuals stratified for common factors such as, but notlimited to, age, sex, age, body mass index (BMI), bone density and/or(2) their own motion at known dates post joint implant.

With respect to macroscopic instability, it is understood that theabsolute abnormal motion in the 5 mm to 10 cm range may be correlatedwith clinical data and further sub-ranges may be used to identifysub-clinical and clinically significant abnormal motion. Whereassub-clinical abnormal motion may be watched for further change,clinically significant abnormal motion and instability will necessitateintervention to resolve patient symptoms. The intervention may take theform of the patient being provided with external support agents such asbraces, custom shoes, and/or orthodics to provide the joint withadditional stability. Pharmacologic therapy may be used to addresssymptoms of pain and inflammation of the joint. The clinician may alsoprescribe physical therapy in lieu of, or in concert with, these devicesto further enhance surrounding musculoskeletal structures to alleviatethe issue.

With respect to microscopic instability, it is understood that absoluteabnormal motion in the 0.1 mm to 2 cm range may be correlated withclinical data and further sub-ranges may be used to identifysub-clinical and clinically significant abnormal motion. Whereassub-clinical abnormal motion may be watched for further change,clinically significant abnormal motion and instability will necessitateintervention to resolve patient symptoms. As described above, theintervention may take the form of the patient receiving a custom polymerinsert designed to re-establish proper motion of the joint and contactwith the opposing articulating surface. As opposed to letting the jointdegrade to a point that a complete revision is necessary, this solutionpresents the opportunity for earlier intervention with a less invasiveprocedure without the need to remove and replace the cemented (oruncemented) metal, prosthetic components. Resolving the microscopicinstability may also take the form of techniques to stabilize the TJAmetallic components implanted within the adjacent bone. One example ofsuch a procedure involves placing the patient under sedation (consciousor full), and using 3 or more k-wires placed using imaging modalitiessuch as bi-plane fluoroscopy, ultrasound, or other methods known tothose skilled in the art through the skin, muscle, cortical bone, andcancellous bone until they contact the TJA stem and/or distal surface ofthe TJA component, the TJA component may be re-positioned into theproper plane within the bone. Electrodes may also be attached to thek-wires to insure neural structures are not negatively impacted duringthe k-wire insertion process. Once the k-wires have been manipulated tore-position the TJA component, fenestrated screws may be advanced overthe k-wire, engaged in the bone for purchase and in contact with the TJAcomponent (typically the stem containing the intelligent implant). Insome cases, prior to insertion of the fenestrated screw, the proximalsurface of the fenestrated screw can be attached to an external conduitcapable of delivering a flowable material such as bone cement, biologicagents and growth factors (such as BMP), bone allograft material(autologous or xenographic), synthetic bone graft material, or othermaterial to facilitate further stabilization of the TJA component andassociated stem within the bone. It is also understood adjustments toboth the polymer insert and the other TJA components may be needed toresolve the microinstability.

In one embodiment, the present disclosure provides a method fordetermining a condition, either a clinical condition or a subclinicalcondition, in a patient having an implanted artificial joint, comprisinga) analyzing movement of an implanted artificial joint, and b) comparingsaid movement vs. previous/standardized norms. The method of the presentdisclosure also provides that the implanted artificial joint isreferring to an implanted intelligent prothesis as described herein,where the prosthesis is implanted in a bone (e.g., a tibia) adjacent toa joint (e.g., a knee joint). The movement of the implanted artificialjoint may be analyzed by making measurements using a sensor coupled tothe implanted artificial joint during a monitoring session wherein theartificial joint moves, where the measurements providemonitoring-session-data that may undergo processing to provideinformation about the movement, e.g., motion modes and optionallyadditional metric values. Optionally, the sensor may be anaccelerometer, i.e., one or more accelerometers. Optionally, themovement of the implanted artificial joint is relative to theenvironment of the patient, e.g., the patient's residence, such as whenthe patient sits down, stands up, or walks across the floor. Thesemovements may be analyzed based upon data obtained during one or moremonitoring sessions, to thereby provide an initial description of thecondition of the implant. Subsequent movements may then be analyzedbased upon data obtained during one or more monitoring sessions, tothereby provide a subsequent description of the condition of theimplant, where the subsequent description is compared to the initialdescription (also referred to as the previous/standardized norms) to seeif there has been a change in the condition of the implant, and toprovide information about the nature of that change. For example, thepresent disclosure provides a method for determining joint loosening ina patient having an implanted artificial joint, comprising a) analyzingmovement of an implanted artificial joint, and b) comparing saidmovement vs. previous/standardized norms. For example, the presentdisclosure provides a method for determining loosening of an intelligentprosthesis that has been implanted in a patient, comprising a) analyzingmovement of an implanted prosthesis, and b) comparing said movement vs.previous/standardized norms

The following are exemplary embodiments of the present disclosure:

-   1) A method for identifying a clinical or subclinical condition    associated with an implant in a patient, the method comprising:    -   a) monitoring a first motion of the implant during a first        monitoring session using a sensor which is directly coupled to        the implant, to provide a first monitoring-session data for the        first motion;    -   b) monitoring a second motion of the implant during a second        monitoring session using the sensor, to provide a second        monitoring-session-data for the second motion; and    -   c) comparing the first monitoring-session data or a product        thereof to the second monitoring-session-data or a product        thereof, to provide a comparison that is indicative of a        clinical or subclinical condition associated with the implant.-   2) The method of embodiment 1 wherein the clinical or subclinical    condition is a loosening of the implant. The loosening may be motion    of prosthesis within the surrounding bone or cement, e.g., the    implant becomes separated from the host bone due, e.g., to    periprosthetic lucency or periprosthetic osteolysis.-   3) The method of embodiment 1 wherein the clinical or subclinical    condition is a malalignment, which may refer to sub-optimal    positioning of a prosthetic component, or a realignment of the    implant, which may refer to a change over time in alignment of    prosthetic component.-   4) The method of embodiment 1 wherein the clinical or subclinical    condition is deformation of the implant, where the deformation maybe    a wearing down of the implant.-   5) The method of embodiment 1 wherein the patient is asymptomatic    for the condition, and the comparison of the first and second data    or products thereof indicate that the condition has occurred between    the first and second monitoring sessions.-   6) The method of embodiment 1 wherein the patient is asymptomatic    for loosening of the implant, and the comparison of the first and    second data or products thereof indicate that the implant has    loosened between the first and second monitoring sessions.-   7) The method of embodiment 1 wherein the patient is asymptomatic    for realignment of the implant, and comparison of the first and    second data or products thereof indicate that the implant has    changed alignment between the first and second monitoring sessions.-   8) The method of embodiment 1 wherein the patient is asymptomatic    for deformation of the implant, and comparison of the first and    second data or products thereof indicate that the implant has    deformed between the first and second monitoring sessions.-   9) A method for treating a clinical or subclinical condition    associated with an implant in a patient, comprising:    -   a) identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b) attaching corrective external bracing to patient to restore        proper alignment and/or enhanced stability to the implant.-   10) The method of embodiment 9 wherein the corrective external    bracing has been specifically tailored to the patient and the    subclinical condition.-   11) A method for treating a clinical or subclinical condition    associated with an implant in a patient, comprising:    -   a) identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b) contacting the implant with a fixation system to retard        progression of the subclinical condition.-   12) The method of embodiment 11 wherein the fixation system    comprises hardware selected from a K-wire, pin, screw, plate and    intramedullary device.-   13) The method of embodiment 11 wherein a screw is located through a    bone that holds the implant, where a terminus of the screw pushes    against a surface of the implant to retard movement of the implant,    where a screw is selected from one, two, three, four, five, six,    seven, eight, nine, ten, eleven, twelve, thirteen, fourteen,    fifteen, sixteen, seventeen, eighteen, nineteen and twenty screws.-   14) The method of embodiment 11 wherein the fixation system    comprises bone cement.-   15) A method for treating a clinical or subclinical condition    associated with an implant in a patient, comprising:    -   a) identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b) contacting the implant with a tamp, where the contacting        changes a location of the implant within the patient.-   16) The method of embodiment 15 wherein the subclinical condition is    a realignment of the implant.-   17) A method for treating a clinical or subclinical condition    associated with an implant in a patient, comprising:    -   a) identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b) implanting an insert adjacent to a component of the implant,        where the insert modifies forces acting on the component of the        implant.-   18) The method of embodiment 17 wherein the insert is a tibial    insert.-   19) The method of embodiment 17 wherein the insert is a tibial    insert having (i) a lateral side with a minimum thickness and (ii) a    medial side with a minimum thickness that is non-identical to the    minimum thickness of the lateral side.-   20) A method for treating a clinical or subclinical condition    associated with an implant in a patient, comprising:    -   a) identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b) delivering a pro-osteointegration agent to a location        surrounding the implant.-   21) The method of embodiment 20 wherein the pro-osteointegration    agent is selected from autologous bone graft, xenograph bone graft,    synthetic bone graft, bone pastes, bone growth factor, and growth    factor.-   22) A method for treating a clinical or subclinical condition    associated with an implant in a patient, comprising:    -   a) identifying an implant in a patient, where the implant has a        clinical or subclinical condition; and    -   b) delivering an anti-bacterial agent to a location surrounding        the implant.-   23) The method of embodiment 22 wherein the anti-bacterial agent is    compounded in a sustained release form.-   24) The method of any of embodiments 1-23 wherein the implant is an    intelligent implant.-   25) The method of embodiments 1-23 wherein the implant is selected    from a knee implant, a hip implant and a shoulder implant.-   26) The method of any of embodiments 1-23 wherein the product of the    monitoring-session data comprises a motion mode.-   27) The method of any of embodiments 1-23 wherein the product of the    monitoring-session data comprises a motion mode, and a status of the    implant is determined from the motion mode.-   28) The method of any of embodiments 1-23 wherein the product of the    monitoring-session data comprises a motion mode, and a status of the    patient is determined from the motion mode.-   29) The method of embodiments 1-23 wherein the implant has been    located within the patient for at least 10 weeks prior to the first    monitoring session.-   30) The method of embodiments 1-23 wherein the implant has changed    alignment over a period of at least 2 weeks.-   31) The method of embodiments 1-23 wherein the implant has loosened    over a period of at least two weeks.-   32) The method of embodiments 1-23 wherein the implant has deformed    over a period of at least two weeks.-   33) The method of embodiments 1-23 wherein the implant comprises a    control circuit configured to cause the sensor to generate a sensor    signal at a frequency that is related to a telemedicine code for the    clinical or subclinical condition, and the sensor signal is    generated at the frequency.-   34) The method of embodiments 1-23 wherein the implant comprises a    control circuit configured to cause the sensor to generate a sensor    signal at a frequency that allows a doctor to qualify for payment    under a telemedicine insurance code, and the sensor signal is    generated at the frequency.-   35) The method of embodiments 1-23 wherein the implant comprises a    control circuit configured to cause the sensor to generate a sensor    signal at a frequency that allows a doctor to qualify for full    payment under a telemedicine insurance code, and the sensor signal    is generated at the frequency.-   36) The method of embodiments 1-23 further comprising generating a    sensor signal that is related to the implant at a frequency that    allows (i) a doctor to qualify for full payment available under a    telemedicine insurance code, or (ii) a doctor to qualify for payment    available under a telemedicine insurance code.-   37) A method comprising:    -   a) providing an intelligent prosthesis implanted in a bone        adjacent to a joint of a patient, where an accelerometer is        contained within the intelligent prosthesis, and where the        accelerometer is positioned within the bone;    -   b) moving the implanted intelligent prosthesis relative to an        external environment wherein the patient is located, where the        implanted intelligent prosthesis is moved during a first        monitoring session;    -   c) making first measurements with the accelerometer during the        first monitoring session, where the first measurements provide        first monitoring-session-data or a product thereof which        identifies a status of the implanted intelligent prosthesis at a        time of the first measurements.-   38) The method of embodiment 37 wherein the accelerometer is a    plurality of accelerometers.-   39) The method of embodiment 37 wherein the accelerometer is    selected from a 1-axis accelerometer, a 2-axis accelerometer and a    3-axis accelerometer.-   40) The method of embodiment 37 wherein the accelerometer operates    in a broadband mode.-   41) The method of embodiment 37 wherein the bone is a tibia.-   42) The method of embodiment 37 wherein the accelerometer is located    in a tibial extension of the intelligent prosthesis.-   43) The method of embodiment 37 wherein the implanted intelligent    prosthesis is moved relative to the external environment without an    impact force being applied to the patient or the intelligent    prosthesis during the first monitoring session.-   44) The method of embodiment 37 wherein the external environment    comprises a residence of the patient.-   45) The method of embodiment 37 wherein the external environment    comprises an operating room wherein the intelligent prosthesis has    been implanted into the patient, where the first monitoring session    optionally occurs while the intelligent prosthesis is being    implanted into the patient, and/or the first monitoring session    optionally occurs after the intelligent prothesis has been implanted    into the patient.-   46) The method of embodiment 37 wherein the status of the implanted    intelligent prosthesis is a characterization of the looseness of the    implanted intelligent prosthesis within the bone.-   47) The method of embodiment 37 wherein the status of the implanted    intelligent prosthesis is a characterization of the alignment of the    implanted intelligent prosthesis within the bone.-   48) The method of embodiment 37 wherein the status of the implanted    intelligent prosthesis is a characterization of the wear of the    implanted intelligent prosthesis.-   49) The method of embodiment 37 wherein the status of the implanted    intelligent prosthesis is a characterization of bacterial infection    of a region within the bone adjacent to the implanted intelligent    prosthesis.-   50) The method of embodiment 37 wherein the status of the implanted    intelligent prosthesis indicates a subclinical condition.-   51) The method of embodiment 37 wherein step b) is repeated after a    waiting period, where the repeat of step b) comprises moving the    implanted intelligent prosthesis relative to an external environment    wherein the patient is located, where the implanted intelligent    prosthesis is moved during a second monitoring session, and wherein    second measurements are made with the accelerometer during the    second monitoring session, where the second measurements provide    second monitoring-session-data or a product thereof which identifies    a status of the implanted of the implanted intelligent prosthesis at    the time of the second measurements.-   52) The method of embodiment 37 wherein step b) is repeated a    plurality of times, the plurality of times separated from one    another by identical or non-identical waiting periods, where the    repeating of step b) comprises moving the implanted intelligent    prosthesis relative to an external environment wherein the patient    is located, where the implanted intelligent prosthesis is moved    during a plurality of monitoring sessions, and wherein measurements    are made with the accelerometer during each of the plurality of    monitoring sessions, where the measurements provide a plurality of    monitoring-session-data or products thereof, each of which    monitoring-session data or product thereof identifies a status of    the implanted intelligent prosthesis at the time of the    measurements.-   53) The method of embodiment 37 wherein step b) is repeated a    plurality of times, the plurality of times separated from one    another by identical or non-identical waiting periods, where the    repeating of step b) comprises moving the implanted intelligent    prosthesis relative to an external environment wherein the patient    is located, where the implanted intelligent prosthesis is moved    during a plurality of monitoring sessions, and wherein measurements    are made with the accelerometer during each of the plurality of    monitoring sessions, where the measurements provide a plurality of    monitoring-session-data or products thereof, each of which    identifies a status of the implanted of the implanted intelligent    prosthesis at the time of the measurements; where the plurality of    monitoring-session data taken together indicate a change in the    status of the implanted intelligent prosthesis during the time when    the plurality of monitoring sessions occurred.-   54) The method of embodiment 53 wherein the change in the status is    indicative of a healing of bone tissue surrounding the implanted    intelligent prosthesis.-   55) The method of embodiment 53 wherein the change in the status is    indicative of an infection of the tissue surrounding the implanted    prosthesis.-   56) The method of embodiment 53 wherein the change in the status is    indicative of a loosening of the implanted intelligent prothesis    within the bone.-   57) The method of embodiment 53 wherein the change in status is    indicative of wear of the implanted intelligent prosthesis.-   58) The method of embodiment 53 wherein the change in status is    indicative of deformation of the implanted intelligent prosthesis.-   59) The method of embodiment 53 wherein the change in status is    indicative of malalignment of the implanted intelligent prosthesis.-   60) The method of embodiment 53 wherein the change in status is    indicative of a change in alignment of the implanted intelligent    prosthesis.-   61) The method of embodiment 53 wherein the change in status is    indicative of bone erosion of the bone adjacent to the implanted    intelligent prosthesis.-   62) The method of embodiment 53 wherein the change in status is    indicative of a subclinical condition.-   63) The method of embodiment 53 wherein the change in status is    indicative of a clinical condition.-   64) The method of embodiment 53 wherein step b) is repeated 2 to 14    times over a 2 to 4 week period.-   65) The method of embodiment 53 wherein step b) is repeated 2-30    times, e.g., 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12    or 13 or 14 times over a 2 week period.-   66) The method of embodiment 53 wherein step b) is repeated on a    daily basis.-   67) The method of any of embodiments 1-66 wherein the condition is a    subclinical condition and the patient is asymptomatic for the    condition of the implant.

Thus, in one embodiment the present disclosure provides a method thatincludes providing an intelligent prosthesis implanted in a boneadjacent to a joint of a patient (step a), where an accelerometer iscontained within the intelligent prosthesis, and where the accelerometeris positioned within the bone. This implanted intelligent prosthesis isthen moved relative to an external environment wherein the patient islocated (step b), where the patient's residence or an operating roomwhere the prosthesis has been implanted are two exemplary externalenvironments, i.e., environments external to the patient. The movementmay be, e.g., at least an inch, or at least 2, or 3, or 4, or 5, or 6,or 7, or 8, or 9, or 10, or 11, or 12 inches. The implanted intelligentprosthesis is moved during a first monitoring session. Firstmeasurements are made with the accelerometer during the first monitoringsession (step c), where the first measurements provide firstmonitoring-session-data or a product thereof. This data or productthereof provides or identifies a status or condition of the implantedintelligent prosthesis at a time of the first measurements. For example,the status may be a certain looseness (or lack of looseness) in theprosthesis as it is seated in the bone of the patient. As anotherexample, the status may be a certain alignment of the prosthesis withinthe bone of the patient. This information may provide a baseline statusfor the prosthesis within the patient, where subsequent monitoringsessions may be performed after a waiting period to obtain subsequentmonitoring-session-data or a product thereof, which may provide oridentify a status or condition of the implanted prosthesis at the timewhen the subsequent monitoring sessions are performed. The waitingperiod may be, e.g., 23 hours, so that the monitoring sessions areperformed on a daily basis. However, the waiting period may be more orless than 23 hours. For example, a waiting period may be shorter than 23hours, e.g., on the scale of 1-10 hours, or 11-22 hours, or may belonger than 23 hours, e.g., 2-14 days, or 1-4 weeks, or 1-6 months.Generally, in order to identify the condition while it is still asub-clinical condition, the waiting period may be relatively short,e.g., the monitoring sessions may be performed on a daily basis.

Optionally, in the method, the method and/or the implant used in themethod may be further described by one or more of: the accelerometer isa plurality of accelerometers; the accelerometer is selected from a1-axis accelerometer, a 2-axis accelerometer and a 3-axis accelerometer;the accelerometer operates in a broadband mode; the bone is a tibia andthe sensor is located within a tibial extension of the tibialintelligent prosthesis; the implanted intelligent prosthesis is movedrelative to the external environment without an impact force beingapplied to either the patient or the intelligent prosthesis during thefirst monitoring session, in other words, nothing external to thepatient causes a movement of the intelligent implant; the externalenvironment comprises a residence of the patient; the externalenvironment comprises an operating room wherein the intelligentprosthesis has been implanted into the patient; the status of theimplanted intelligent prosthesis is a characterization of the loosenessof the implanted intelligent prosthesis within the bone; the status ofthe implanted intelligent prosthesis is a characterization of thealignment of the implanted intelligent prosthesis within the bone; thestatus of the implanted intelligent prosthesis is a characterization ofthe wear of the implanted intelligent prosthesis; the status of theimplanted intelligent prosthesis is a characterization of bacterialinfection of a region within the bone adjacent to the implantedintelligent prosthesis; the status of the implanted intelligentprosthesis indicates a subclinical condition.

As mentioned, step b) may repeated after a waiting period, where therepeat of step b) comprises moving the implanted intelligent prosthesisrelative to an external environment wherein the patient is located,where the implanted intelligent prosthesis is moved during a secondmonitoring session, and wherein second measurements are made with theaccelerometer during the second monitoring session, where the secondmeasurements provide second monitoring-session-data or a product thereofwhich identifies a status of the implanted of the implanted intelligentprosthesis at the time of the second measurements.

As mentioned, step b) may be repeated a plurality of times, theplurality of times separated from one another by identical ornon-identical waiting periods, where the repeating of step b) comprisesmoving the implanted intelligent prosthesis relative to an externalenvironment wherein the patient is located, where the implantedintelligent prosthesis is moved during a plurality of monitoringsessions, and wherein measurements are made with the accelerometerduring each of the plurality of monitoring sessions, where themeasurements provide a plurality of monitoring-session-data or productsthereof, each of which identifies a status of the implanted of theimplanted intelligent prosthesis at the time of the measurements.

As mentioned, step b) may be repeated a plurality of times, theplurality of times separated from one another by identical ornon-identical waiting periods, where the repeating of step b) comprisesmoving the implanted intelligent prosthesis relative to an externalenvironment wherein the patient is located, where the implantedintelligent prosthesis is moved during a plurality of monitoringsessions, and wherein measurements are made with the accelerometerduring each of the plurality of monitoring sessions, where themeasurements provide a plurality of monitoring-session-data or productsthereof, each of which identifies a status of the implanted of theimplanted intelligent prosthesis at the time of the measurements;wherein the plurality is optionally selected from 2 to 20 monitoringsessions, and where the plurality of monitoring-session data takentogether indicate a change in the status of the implanted intelligentprosthesis during the time when the plurality of monitoring sessionsoccurred.

The foregoing methods, e.g., methods of embodiments 1-67, may identify aproblem with an intelligent implanted prosthesis, e.g., a clinical orsubclinical condition such as loosening of the implant, change inalignment of the implant and/or deformation of the implant.

In one embodiment of each of the foregoing methods, e.g., the methods ofembodiments 1-67, the implant is an implantable medical device, thedevice comprising: at least one sensor configured to generate a sensorsignal; and a control circuit configured to cause the at least onesensor to generate the sensor signal at a frequency that is related to atelemedicine code. The telemedicine code may indicate the clinical orsubclinical condition associated with the implanted medical device inthe patient.

In one embodiment of each of the foregoing methods, e.g., the methods ofembodiments 1-67, the implant is an implantable medical device, thedevice comprising at least one sensor configured to generate a sensorsignal; and a control circuit configured to cause the at least onesensor to generate the sensor signal at a frequency that allows a doctorto qualify for payment under a telemedicine insurance code.

In one embodiment in each of the foregoing methods, e.g., the methods ofembodiments 1-67, the implant is an implantable medical device, thedevice comprising at least one sensor configured to generate a sensorsignal; and a control circuit configured to cause the at least onesensor to generate the sensor signal at a frequency that allows a doctorto qualify for full payment under a telemedicine insurance code.

The foregoing methods, e.g., the methods of embodiments 1-67, mayidentify a problem with an intelligent implanted prosthesis, e.g., aclinical or subclinical condition such as loosening of the implant,change in alignment of the implant and/or deformation of the implant. Inaddition to identifying the problem, each method may include generationof a telemedicine code as descried herein. For example, in oneembodiment of each of the foregoing methods, the method furthercomprises generating a sensor signal that is related to the implantedmedical device at a frequency that allows a doctor to qualify forpayment available under a telemedicine insurance code. As anotherexample, in one embodiment of each of the foregoing methods, the methodfurther comprises generating a sensor signal that is related to theimplanted medical device at a frequency that allows a doctor to qualifyfor full payment available under a telemedicine insurance code.

For example, the present disclosure provides a method for identifying aclinical or subclinical condition associated with an implant in apatient, the method comprising: monitoring a first motion of the implantduring a first monitoring session using a sensor which is directlycoupled to the implant, to provide a first data description of the firstmotion; monitoring a second motion of the implant during a secondmonitoring session using the sensor, to provide a second datadescription of the second motion; comparing the first and second datadescriptions to identify a clinical or subclinical condition associatedwith the implant; and generating a sensor signal that is related to theimplant at a frequency that allows a doctor to qualify for full paymentavailable under a telemedicine insurance code.

The foregoing methods, e.g., the methods of embodiments 1-67 mayidentify a problem with an intelligent implanted prosthesis, e.g., aclinical or subclinical condition such as loosening of the implant,change in alignment of the implant and/or deformation of the implant.The foregoing methods make use of an intelligent implant having asensor, where a sensor refers to one or more sensors, and likewiserefers to at least one sensor. The intelligent implant may haveadditional features as disclosed herein.

For example, the intelligent implant may further comprise a controlcircuit configured to cause the sensor, or another sensor which is acomponent of the implant, to generate a sensor signal at a frequencythat allows a doctor to qualify for payment under a telemedicineinsurance code.

As another example, the intelligent implant may be described ascomprising a housing; and an implanted circuit disposed in the housing,where the circuit is configured to (i) generate at least one firstsignal representative of a movement; (ii) determine whether the signalmeets at least one first criterion; and (iii) send the signal to aremote location in response to determining that the signal meets the atleast one first criterion, as described herein. The intelligent implantor a component thereon, e.g., the implanted circuit disposed in thehousing of the intelligent implant, may be further described by one ormore of the following: the housing includes a tibial extension; themovement includes a movement of the patient; the movement includes thepatient walking; the at least one first criterion includes that thesignal represents the movement for at least a threshold duration; the atleast one first criterion includes that the signal represents themovement for at least a threshold number of events; the movementincludes the patient walking, and the at least one first criterionincludes that the signal represents the movement for at least athreshold number of steps taken by the patient; the implanted circuit isfurther configured to determine whether the movement meets at least onesecond criterion before determining whether the signal meets the atleast one first criterion, and to determine whether the signal meets theat least one first criterion in response to determining that themovement meets the second criterion, particularly wherein the at leastone second criterion includes that the movement is the patient walking;the implanted circuit is further configured to determine, in response tothe signal, whether the movement meets at least one second criterionbefore determining whether the signal meets the at least one firstcriterion, and to determine whether the signal meets the at least onefirst criterion in response to determining that the movement meets thesecond criterion; the implanted circuit is further configured todetermine, in response to the signal, whether the movement meets atleast one second criterion, and to cease generating the signal inresponse to determining that the movement does not meet the at least onesecond criterion; the implanted circuit is further configured todetermine, in response to the signal, whether the movement meets atleast one second criterion, and to cease generating the signal beforedetermining whether the signal meets the at least one first criterion inresponse to determining that the movement does not meet the at least onesecond criterion; the implanted circuit is further configured to storethe signal in response to determining that the signal meets the at leastone first criterion, and to send the stored signal to the remotelocation; the implanted circuit is further configured to encrypt thesignal before sending the signal to the remote location; the implantedcircuit is further configured to encode the signal before sending thesignal to the remote location; the implanted circuit is furtherconfigured to generate a message that includes the signal; and whereinsending the signal includes sending the message; and the implantedcircuit is further configured to generate a data packet that includesthe signal; and wherein sending the message includes sending the datapacket to the remote location.

In additional embodiments, the present disclosure provides a methodcomprising generating a sensor signal in response to a movement of apatient in which an intelligent prosthesis is implanted. The followingare exemplary of such methods of the present disclosure:

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;and transmitting the sensor signal to a remote location, wherein thesensor signal identifies a clinical or subclinical condition associatedwith the implanted intelligent prosthesis, particularly where thepatient is asymptomatic for the condition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;sampling the sensor signal; and transmitting the samples to a remotelocation, wherein the sensor signal identifies a clinical or subclinicalcondition associated with the implanted intelligent prosthesis,particularly where the patient is asymptomatic for the condition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;determining whether the sensor signal represents a qualified event; andtransmitting the signal to a remote location in response to determiningthat the sensor signal represents a qualified event, wherein the sensorsignal identifies a clinical or subclinical condition associated withthe implanted intelligent prosthesis, particularly where the patient isasymptomatic for the condition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;receiving a polling signal from a remote location; and transmitting thesensor signal to the remote location in response to the polling signal,wherein the sensor signal identifies a clinical or subclinical conditionassociated with the implanted intelligent prosthesis, particularly wherethe patient is asymptomatic for the condition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;generating a message that includes the sensor signal or datarepresentative of the sensor signal; and transmitting the message to aremote location, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;generating a data packet that includes the sensor signal or datarepresentative of the sensor signal; and transmitting the data packet toa remote location, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;encrypting at least a portion of the sensor signal or datarepresentative of the sensor signal; and transmitting the encryptedsensor signal to a remote location, wherein the sensor signal identifiesa clinical or subclinical condition associated with the implantedintelligent prosthesis, particularly where the patient is asymptomaticfor the condition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;encoding at least a portion of the sensor signal or data representativeof the sensor signal; and transmitting the encoded sensor signal to aremote location, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: generating a sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;transmitting the sensor signal to a remote location; and entering animplantable circuit associated with the prosthesis into a lower-powermode after transmitting the sensor signal, wherein the sensor signalidentifies a clinical or subclinical condition associated with theimplanted intelligent prosthesis, particularly where the patient isasymptomatic for the condition.

In additional embodiments, the present disclosure provides a methodcomprising generating a sensor signal and/or receiving a sensor signalfrom an implanted intelligent prosthesis. The following are exemplary ofsuch methods of the present disclosure, where the sensor signal may bewithin a data packet.

A method comprising: generating a first sensor signal in response to amovement of a patient in which an intelligent prosthesis is implanted;transmitting the first sensor signal to a remote location; entering atleast one component of an implantable circuit associated with theprosthesis into a lower-power mode after transmitting the sensor signal;and generating a second sensor signal in response to a movement of thepatient after an elapse of a low-power-mode time for which theimplantable circuit is configured, wherein the sensor signal identifiesa clinical or subclinical condition associated with the implantedintelligent prosthesis, particularly where the patient is asymptomaticfor the condition.

A method comprising: receiving a sensor signal from an intelligentprosthesis implanted in a patient; and transmitting the received sensorsignal to a destination, wherein the sensor signal identifies a clinicalor subclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: sending an inquiry to an intelligent prosthesisimplanted in a patient; receiving a sensor signal from the intelligentprosthesis after sending the inquiry; and transmitting the receivedsensor signal to a destination, wherein the sensor signal identifies aclinical or subclinical condition associated with the implantedintelligent prosthesis, particularly where the patient is asymptomaticfor the condition.

A method comprising: receiving a sensor signal and at least oneidentifier from an intelligent prosthesis implanted in a patient;determining whether the identifier is correct; and transmitting thereceived sensor signal to a destination in response to determining thatthe identifier is correct, wherein the sensor signal identifies aclinical or subclinical condition associated with the implantedintelligent prosthesis, particularly where the patient is asymptomaticfor the condition.

A method comprising: receiving a message including a sensor signal froman intelligent prosthesis implanted in a patient; decrypting at least aportion of the message; and transmitting the decrypted message to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a message including a sensor signal froman intelligent prosthesis implanted in a patient; decoding at least aportion of the message; and transmitting the decoded message to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a message including a sensor signal froman intelligent prosthesis implanted in a patient; encoding at least aportion of the message; and transmitting the encoded message to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a message including a sensor signal froman intelligent prosthesis implanted in a patient; encrypting at least aportion of the message; and transmitting the encrypted message to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a data packet including a sensor signalfrom an intelligent prosthesis implanted in a patient; decrypting atleast a portion of the data packet; and transmitting the decrypted datapacket to a destination, wherein the sensor signal identifies a clinicalor subclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a data packet including a sensor signalfrom an intelligent prosthesis implanted in a patient; decoding at leasta portion of the data packet; and transmitting the decoded data packetto a destination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a data packet including a sensor signalfrom an intelligent prosthesis implanted in a patient; encoding at leasta portion of the data packet; and transmitting the encoded data packetto a destination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a data packet including a sensor signalfrom an intelligent prosthesis implanted in a subject; encrypting atleast a portion of the data packet; and transmitting the encrypted datapacket to a destination, wherein the sensor signal identifies a clinicalor subclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a sensor signal from an intelligentprosthesis implanted in a patient; decrypting at least a portion of thesensor signal; and transmitting the decrypted sensor signal to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a sensor signal from an intelligentprosthesis implanted in a patient; decoding at least a portion of thesensor signal; and transmitting the decoded sensor signal to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising receiving a sensor signal from an intelligentprosthesis implanted in a patient; encoding at least a portion of thesensor signal; and transmitting the encoded sensor signal to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

A method comprising: receiving a sensor signal from an intelligentprosthesis implanted in a patient; encrypting at least a portion of thesensor signal; and transmitting the encrypted sensor signal to adestination, wherein the sensor signal identifies a clinical orsubclinical condition associated with the implanted intelligentprosthesis, particularly where the patient is asymptomatic for thecondition.

In addition, the present disclosure provides a method for identifying aclinical or subclinical condition associated with an implant in apatient, such as a looseness of the implant, or a malalignment of theimplant. The method includes monitoring a first motion of the implantduring a first monitoring session using a sensor which is directlycoupled to the implant. The first motion may be, e.g., a movement of theimplant relative to the environment within which the patient having theimplant is disposed. The monitoring provides a first monitoring-sessiondata or a product thereof, for the first motion. The monitoring iscarried out by a monitoring-session-data collection, analysis, andstatus-reporting system implemented as a component of one or morecomputer systems, each computer system having one or more processors,one or more memories, one or more network connections, and access to oneor more mass-storage devices, as described herein. As also describedherein, the monitoring may include: receiving monitoring-session-data,optionally including acceleration data generated by one or more sensorswithin or proximal to a prosthesis attached to or implanted within apatient, from an external monitoring-session-data source; storing thereceived monitoring-session-data in one or more of the one or morememories and one or more mass-storage devices; determining componenttrajectories representing motion modes, and optionally representingadditional metric values, from the monitoring session data; determiningat least one of a prosthesis status and a patient status from the motionmodes and optionally from the additional metric value; distributing thedetermined prosthesis status and/or patient status to target computersystems through the network connections; and when indicated by thedetermined prosthesis status and/or patient status, distributing one ormore alarms and events to target computer systems through the networkconnections.

The following Examples are offered by way of illustration and not by wayof limitation.

EXAMPLES Example 1 Surgical Method for Replacement of a Tibial Insert

Once a decision to change the polymeric insert is made, the patient isprepared for surgery. This includes not only medical clearance butevaluation of the kinematic data to determine the direction, amount andpattern of abnormal motion and instability. The data obtained from thepatient's intelligent implant will then be used to determine thespecific characteristics (as described previously) of the polyethyleneinsert so as to resist or eliminate the abnormal motion or instabilityobserved in the patient. This would include polymeric inserts ofincreased size and constraint and/or offset designs to adjust coronalalignment as determined by the intelligent implant joint movementanalysis.

For example, in a TKA patient, a new incision is made through theprevious incision site and dissection is continued to the level of theextensor mechanism. The arthrotomy is performed and the proximal tibiais exposed. Maneuvers are then performed to place the tibia in a forwardposition so as to permit exchange of the tibial tray. The existing,ineffective, tibial insert is removed, and a customized tibial insert isimplanted in its place. Trial reductions are then performed, and bestfit is determined. The selection of the correct tibial insert is madenot only by the surgeon based upon the clinical feel and stability ofthe TKA containing the new tibial insert, but is also informed by dataobtained from kinematic analysis performed intraoperatively by theintelligent TKA. Several different tibial inserts might be tested beforedetermining which one best eliminates the abnormal movement and/orinstability. The new, preferred tibial insert is then placed into thetibial tray, standard closure is performed, and post-operativerehabilitation is initiated.

Example 2 Surgical Method for Realignment of a Misaligned ImplantedArtificial Joint Using a Filler

Traditional methods of TJA malalignment involved either prosthesisretention until failure or either partial or complete revision. Theserevisions result in not only a major invasive procedure that is not onlycostly, but can also lead to increased complications such as bone loss,decreased performance, infection and poor results compared to a primaryTotal Joint Replacement.

Intraoperative malalignment of a TJA implant can be achieved withprosthesis retention through an intraoperative osteotomy procedure.Joint kinematics obtained from the intelligent TJA are reviewedpreoperatively and a decision is made on the direction and degree ofprosthesis adjustment required. The adjustment begins by making a smallosteotomy around the medial and/or the lateral aspect of the misalignedTJA component (typically the stem of the prosthesis). A series of tampsare then inserted via the osteotomy and into contact with the TJAcomponent. The tamps are carefully advanced in order to adjust theprosthesis alignment to the desired “new” location and intraoperativekinematics are performed to confirm placement and ensure adequatecorrection. A small boney window is then made, and either liquid bonecement, bone allograft material (autologous or xenographic), syntheticbone graft material, or other filler material is then injected into thespace between the implant and the bone to solidify the prosthesis in itsnew position. Standard closure and post-operative rehabilitation is theninitiated.

The devices, methods, systems etc. of the present disclosure have beendescribed broadly and generically herein. Each of the narrower speciesand subgeneric groupings falling within the generic disclosure also formpart of the present disclosure. This includes the generic description ofthe devices, methods, systems etc. of the present disclosure with aproviso or negative limitation removing any subject matter from thegenus, regardless of whether or not the excised material is specificallyrecited herein.

The following are some exemplary embodiments of the present disclosure,numbered for convenience:

1. A tibial insert for a implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe medial side of the implant, as compared to the lateral side.

2. A tibial insert for a implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe lateral side of the implant, as compared to the medial side.

3. A tibial insert for a implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe anterior side of the implant, as compared to the posterior side.

4. A tibial insert for a implantable knee prosthesis, comprising atibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mm thicker onthe posterior side of the implant, as compared to the anterior side.

5. A tibial insert/articular spacer/for a implantable knee prosthesis,comprising a tibial insert that is 1, 2, 3, 4, 5, 6, 7, 8, 9, or, 10 mmthicker on the medial, lateral, anterior and/or posterior side of theimplant.

6. The tibial insert according to any one of embodiments 1-5, whereinsaid tibial insert is composed of polyethylene, or polyetheretherketone(PEEK).

7. The tibial insert according to any one of embodiments 1-6 whereinsaid tibial insert is customized to a patient.

8. The tibial insert according to any one of embodiments 1 to 7 whereinsaid insert is manufactured by 3-D printing, or, by molding.

In the embodiments 1-8, which are directed to a tibial insert for animplantable knee prosthesis, the insert will have a medial side, alateral side, an anterior side and a posterior side. The embodimentsprovide for asymmetry in the thickness of the tibial insert, such thatthe insert is thicker at a location on one side of the insert than it isat an equivalent location at the opposing side of the insert, e.g., thecenter of the medial side of the insert as compared to the center of thelateral side of the insert, or the center of the anterior side of theinsert as compared to the center of the posterior side of the insert.This asymmetry can, e.g., compensate for malalignment in positioning ofthe implanted knee prosthesis, such that forces are better balanced.

9. An implantable medical device, comprising:

a circuit configured to be fixedly attached to an implantable prostheticdevice;

a power component; and

a device configured to uncouple the circuit from the power component.

10. An implantable medical device, comprising:

a circuit configured to be fixedly attached to an implantable prostheticdevice;

a battery; and

a fuse coupled between the circuit and the battery.

11. A method, comprising electrically opening a fuse that is disposedbetween a circuit and a battery, at least the fuse and the circuit beingdisposed on an implanted prosthetic device.

12. An implantable medical device, comprising:

at least one sensor configured to generate a sensor signal; and

a control circuit configured to cause the at least one sensor togenerate the sensor signal at a frequency that is related to atelemedicine code.

13. An implantable medical device, comprising:

at least one sensor configured to generate a sensor signal; and

a control circuit configured to cause the at least one sensor togenerate the sensor signal at a frequency that allows a doctor toqualify for payment under a telemedicine insurance code.

14. An implantable medical device, comprising:

at least one sensor configured to generate a sensor signal; and

a control circuit configured to cause the at least one sensor togenerate the sensor signal at a frequency that allows a doctor toqualify for full payment under a telemedicine insurance code.

15. A method, comprising, generating a sensor signal that is related toan implanted medical device at a frequency that allows a doctor toqualify for payment available under a telemedicine insurance code.

16. A method, comprising, generating a sensor signal that is related toan implanted medical device at a frequency that allows a doctor toqualify for full payment available under a telemedicine insurance code.

17. An implantable prosthesis, comprising:

a housing; and

an implantable circuit disposed in the housing and configured togenerate at least one first signal representative of a movement;

to determine whether the signal meets at least one first criterion; and

to send the signal to a remote location in response to determining thatthe signal meets the at least one first criterion.

18. A base station, comprising:

a housing; and

a base-station circuit disposed in the housing and configured toreceive, from an implantable prosthesis, at least first signalrepresentative of a movement;

to send the at least one first signal to a destination;

to receive at least one second signal from a source; and

to send the at least one second signal to the implantable prosthesis.

19. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a current through the fuse exceeding an overcurrent threshold.

20. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a current through the fuse exceeding an overcurrent threshold for atleast a threshold time.

21. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a voltage across the fuse exceeding an overvoltage threshold.

22. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a voltage across the fuse exceeding an overvoltage threshold for atleast a threshold time.

23. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a temperature exceeds an overtemperature threshold.

24. A method, comprising opening a fuse disposed on an implantableprosthesis between a power source and an implantable circuit in responseto a temperature exceeding an overtemperature threshold for at least athreshold length of time.

25. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted; and    -   transmitting the sensor signal to a remote location.

26. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   sampling the sensor signal; and    -   transmitting the samples to a remote location.

27. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   determining whether the sensor signal represents a qualified        event; and    -   transmitting the signal to a remote location in response to        determining that the sensor signal represents a qualified event.

28. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   receiving a polling signal from a remote location; and    -   transmitting the sensor signal to the remote location in        response to the polling signal.

29. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   generating a message that includes the sensor signal or data        representative of the sensor signal; and    -   transmitting the message to a remote location.

30. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   generating a data packet that includes the sensor signal or data        representative of the sensor signal; and    -   transmitting the data packet to a remote location.

31. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   encrypting at least a portion of the sensor signal or data        representative of the sensor signal; and    -   transmitting the encrypted sensor signal to a remote location.

32. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   encoding at least a portion of the sensor signal or data        representative of the sensor signal; and    -   transmitting the encoded sensor signal to a remote location.

33. A method, comprising:

-   -   generating a sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   transmitting the sensor signal to a remote location; and    -   entering an implantable circuit associated with the prosthesis        into a lower-power mode after transmitting the sensor signal.

34. A method, comprising:

-   -   generating a first sensor signal in response to a movement of a        subject in which a prosthesis is implanted;    -   transmitting the first sensor signal to a remote location;    -   entering at least one component of an implantable circuit        associated with the prosthesis into a lower-power mode after        transmitting the sensor signal; and    -   generating a second sensor signal in response to a movement of        the subject after an elapse of a low-power-mode time for which        the implantable circuit is configured.

35. A method, comprising:

-   -   receiving a sensor signal from a prosthesis implanted in a        subject; and    -   transmitting the received sensor signal to a destination.

36. A method, comprising:

-   -   sending an inquiry to a prosthesis implanted in a subject        receiving a sensor signal from a prosthesis after sending the        inquiry; and    -   transmitting the received sensor signal to a destination.

37. A method, comprising:

-   -   receiving a sensor signal and at least one identifier from a        prosthesis implanted in a subject;    -   determining whether the identifier is correct; and    -   transmitting the received sensor signal to a destination in        response to determining that the identifier is correct.

38. A method, comprising:

-   -   receiving a message including a sensor signal from a prosthesis        implanted in a subject;    -   decrypting at least a portion of the message; and    -   transmitting the decrypted message to a destination.

39. A method, comprising:

-   -   receiving a message including a sensor signal from a prosthesis        implanted in a subject;    -   decoding at least a portion of the message; and    -   transmitting the decoded message to a destination.

40. A method, comprising:

-   -   receiving a message including a sensor signal from a prosthesis        implanted in a subject;    -   encoding at least a portion of the message; and    -   transmitting the encoded message to a destination.

41. A method, comprising:

-   -   receiving a message including a sensor signal from a prosthesis        implanted in a subject;    -   encrypting at least a portion of the message; and    -   transmitting the encrypted message to a destination.

42. A method, comprising:

-   -   receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   decrypting at least a portion of the data packet; and    -   transmitting the decrypted data packet to a destination.

43. A method, comprising:

-   -   receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   decoding at least a portion of the data packet; and    -   transmitting the decoded data packet to a destination.

44. A method, comprising:

-   -   receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   encoding at least a portion of the data packet; and    -   transmitting the encoded data packet to a destination.

45. A method, comprising:

-   -   receiving a data packet including a sensor signal from a        prosthesis implanted in a subject;    -   encrypting at least a portion of the data packet; and    -   transmitting the encrypted data packet to a destination.

46. A method, comprising:

-   -   receiving a sensor signal from a prosthesis implanted in a        subject;    -   decrypting at least a portion of the sensor signal; and    -   transmitting the decrypted sensor signal to a destination.

47. A method, comprising:

-   -   receiving a sensor signal from a prosthesis implanted in a        subject;    -   decoding at least a portion of the sensor signal; and    -   transmitting the decoded sensor signal to a destination.

48. A method, comprising:

-   -   receiving a sensor signal from a prosthesis implanted in a        subject;    -   encoding at least a portion of the sensor signal; and    -   transmitting the encoded sensor signal to a destination.

49. A method, comprising:

-   -   receiving a sensor signal from a prosthesis implanted in a        subject;    -   encrypting at least a portion of the sensor signal; and    -   transmitting the encrypted sensor signal to a destination.

50. An implantable circuit for an implantable prosthesis.

51. An implantable prosthesis including an implantable circuit.

52. An implantable prosthesis including a fuse.

53. A base station for communication with an implantable prosthesis.

54. A monitoring-session-data collection, analysis, and status-reportingsystem implemented as a component of one or more computer systems, eachcomputer system having one or more processors, one or more memories, oneor more network connections, and access to one or more mass-storagedevices, the one or more the monitoring-session-data collection,data-analysis, and status-reporting system comprising:

-   -   a monitoring-session-data-receiving component that receives        monitoring-session-data, including acceleration data generated        by sensors within or proximal to a prosthesis attached or        implanted within a patient, from an external        monitoring-session-data source and that stores the received        monitoring-session-data in one or more of the one or more        memories and one or more mass-storage devices;    -   a monitoring-session-data-processing component that        -   prepares the monitoring-session-data for processing,        -   determines component trajectories representing motion modes            and additional metric values from the            monitoring-session-data; and    -   a monitoring-session-data-analysis component that        -   determines a prosthesis status and a patient status from the            motion modes and additional metric values,        -   distributes the determined prosthesis status and patient            status to target computer systems through the network            connections, and        -   when indicated by the determined prosthesis status and            patient status, distributes one or more alarms and events to            target computer systems through the network connections.

55. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data includes:

-   -   a patient identifier;    -   a device identifier;    -   a timestamp;    -   device-configuration data; and    -   an ordered set of data.

56. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 55 wherein the ordered set of datacomprises one of:

-   -   a time sequence of data vectors, each data vector including        numerical values related to linear-accelerations with respect to        three coordinate axes of an internal device coordinate system;        and    -   a time sequence of data vectors, each data vector including        numerical values related to linear-accelerations with respect to        three coordinate axes of a first internal device coordinate        system and including numerical values related to angular        velocities, numerical values related to angular velocities        relative to the first internal device coordinate system or to a        second internal device coordinate system.

57. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-processing component prepares themonitoring-session-data for processing by:

-   -   receiving a time sequence of data vectors, each data vector        including three numerical values related to linear-accelerations        in the directions of three coordinate axes of a first internal        device coordinate system and including three numerical values        related to angular velocities about each axis of the first or a        second internal device coordinate system;    -   when rescaling of the data-vector sequence is needed, rescaling        the numerical values of the data vectors;    -   when normalization of the data-vector sequence is needed,        normalizing the numerical values of the data vectors;    -   when transformation of one or more of the numerical values        related to linear-acceleration and the numerical values related        to angular velocities is needed to relate the numerical values        related to linear-acceleration and the numerical values related        to angular velocities to a common internal coordinate system,        transforming one or more of the numerical values related to        linear-acceleration and the numerical values related to angular        velocities to relate to the common internal coordinate system;        and    -   when the time sequence of data vectors needs to be synchronized        with respect to a fixed-interval time sequence, synchronizing        the data vectors with respect to a fixed-interval time sequence.

58. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-processing component determines componenttrajectories representing motion modes and additional metric values fromthe monitoring-session-data by:

-   -   orienting the prepared monitoring-session-data, comprising data        vectors, each data vector including three numerical values        related to linear-accelerations in the directions of three        coordinate axes of an internal device coordinate system and        including three numerical values related to angular velocities        about each axis of the internal device coordinate system, with        respect to a natural coordinate system;    -   bandpass filtering the oriented data vectors to obtain a set of        data vectors for each of multiple frequencies, including a        normal-motion frequency;    -   determining, from the data vectors for each of the        non-normal-motion frequencies, a spatial amplitude in each of        the coordinate-axis directions of the natural coordinate system;    -   determining, from a basis trajectory for the patient and the        data vectors for the normal-motion frequency, a spatial        amplitude in each of the coordinate-axis directions of the        natural coordinate system; and    -   determining, from the basis trajectory for the patient and the        data vectors for the normal-motion frequency, current        normal-motion characteristics.

59. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 58 wherein determining, from thedata vectors for a frequency, a spatial amplitude in each of thecoordinate-axis directions of the natural coordinate system furthercomprises:

-   -   generating a spatial trajectory from the data vectors;    -   projecting the spatial frequency onto each of the coordinate        axes; and    -   determining the lengths of the protections of the spatial        frequency onto each of the coordinate axes.

60. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-analysis component determines a prosthesisstatus and a patient status from the motion modes and additional metricvalues by:

-   -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

61. The monitoring-session-data collection, analysis, andstatus-reporting system of embodiment 54 wherein themonitoring-session-data-analysis component wherein the one or morealarms and events distributed to target computer systems include:

-   -   an alarm that notifies a medical practitioner or medical        facility of the need, by the patient, of immediate assistance or        intervention; and    -   an event that indicates additional services and/or equipment        needed by the patient that may be handled by various external        computer systems to automatically provide the additional        services and/or equipment to the patient or inform the patient        of the additional services and/or equipment and provide the        patient with information regarding procurement of the additional        services and/or equipment.

62. A method, carried out by a monitoring-session-data collection,analysis, and status-reporting system implemented as a component of oneor more computer systems, each computer system having one or moreprocessors, one or more memories, one or more network connections, andaccess to one or more mass-storage devices, the method comprising:

-   -   receiving monitoring-session-data, including acceleration data        generated by sensors within or proximal to a prosthesis attached        or implanted within a patient, from an external        monitoring-session-data source;    -   storing the received monitoring-session-data in one or more of        the one or more memories and one or more mass-storage devices;    -   determining a prosthesis status and a patient status from the        motion modes and additional metric values,    -   distributing the determined prosthesis status and patient status        to target computer systems through the network connections, and    -   when indicated by the determined prosthesis status and patient        status, distributing one or more alarms and events to target        computer systems through the network connections.

63. The method of embodiment 62 wherein determining a prosthesis statusand a patient status from the motion modes and additional metric valuesfurther comprises:

-   -   preparing the monitoring-session-data for processing,    -   determines component trajectories representing motion modes and        additional metric values from the monitoring-session-data;    -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

64. The method of embodiment 62 wherein preparing themonitoring-session-data for processing further comprises

-   -   receiving a time sequence of data vectors, each data vector        including three numerical values related to linear-accelerations        in the directions of three coordinate axes of a first internal        device coordinate system and including three numerical values        related to angular velocities about each axis of the first or a        second internal device coordinate system;    -   when rescaling of the data-vector sequence is needed, rescaling        the numerical values of the data vectors;    -   when normalization of the data-vector sequence is needed,        normalizing the numerical values of the data vectors;    -   when transformation of one or more of the numerical values        related to linear-acceleration and the numerical values related        to angular velocities is needed to relate the numerical values        related to linear-acceleration and the numerical values related        to angular velocities to a common internal coordinate system,        transforming one or more of the numerical values related to        linear-acceleration and the numerical values related to angular        velocities to relate to the common internal coordinate system;        and    -   when the time sequence of data vectors needs to be synchronized        with respect to a fixed-interval time sequence, synchronizing        the data vectors with respect to a fixed-interval time sequence.

65. The method of embodiment 62 wherein determining componenttrajectories representing motion modes and additional metric values fromthe monitoring-session-data by:

-   -   orienting the prepared monitoring-session-data, comprising data        vectors, each data vector including three numerical values        related to linear-accelerations in the directions of three        coordinate axes of an internal device coordinate system and        including three numerical values related to angular velocities        about each axis of the internal device coordinate system, with        respect to a natural coordinate system;    -   bandpass filtering the oriented data vectors to obtain a set of        data vectors for each of multiple frequencies, including a        normal-motion frequency;    -   determining, from the data vectors for each of the        non-normal-motion frequencies, a spatial amplitude in each of        the coordinate-axis directions of the natural coordinate system;    -   determining, from a basis trajectory for the patient and the        data vectors for the normal-motion frequency, a spatial        amplitude in each of the coordinate-axis directions of the        natural coordinate system; and    -   determining, from the basis trajectory for the patient and the        data vectors for the normal-motion frequency, current        normal-motion characteristics.

66. The method of embodiment 54 wherein determining, from the datavectors for a frequency, a spatial amplitude in each of thecoordinate-axis directions of the natural coordinate system furthercomprises:

-   -   generating a spatial trajectory from the data vectors;    -   projecting the spatial frequency onto each of the coordinate        axes; and    -   determining the lengths of the protections of the spatial        frequency onto each of the coordinate axes.

67. The method of embodiment 54 wherein determining a prosthesis statusand a patient status from the motion modes and additional metric valuesfurther comprises:

-   -   submitting the motion modes and additional metric values to a        decision tree that generates a diagnosis-and-suggestions report;        and    -   packaging the diagnosis-and-suggestions report together with        amplitudes generated for the motion modes, metrics generated        from a normal-motion-frequency trajectory and a base trajectory,        and additional metric values to generate one or both of an        output report and output data values that characterize the        prosthesis status and the patient status.

68. The method of embodiment 54 wherein the one or more alarms andevents distributed to target computer systems include:

-   -   an alarm that notifies a medical practitioner or medical        facility of the need, by the patient, of immediate assistance or        intervention; and    -   an event that indicates additional services and/or equipment        needed by the patient that may be handled by various external        computer systems to automatically provide the additional        services and/or equipment to the patient or inform the patient        of the additional services and/or equipment and provide the        patient with information regarding procurement of the additional        services and/or equipment.

69. A physical data-storage device encoded with computer instructionsthat, when executed by one or more processors within one or morecomputer systems of a monitoring-session-data collection, analysis, andstatus-reporting system, each computer system having one or moreprocessors, one or more memories, one or more network connections, andaccess to one or more mass-storage devices, control themonitoring-session-data collection, analysis, and status-reportingsystem to:

-   -   receive monitoring-session-data, including acceleration data        generated by sensors within or proximal to a prosthesis attached        or implanted within a patient, from an external        monitoring-session-data source.

70. A method for determining joint loosening in a patient having animplanted artificial joint, comprising a) analyzing movement of animplanted artificial joint, and b) comparing said movement vs.previous/standardized norms.

It is also to be understood that as used herein and in the appendedclaims, the singular forms “a,” “an,” and “the” include plural referenceunless the context clearly dictates otherwise, the term “X and/or Y”means “X” or “Y” or both “X” and “Y”, and the letter “s” following anoun designates both the plural and singular forms of that noun. Inaddition, where features or aspects of the present disclosure aredescribed in terms of Markush groups, it is intended, and those skilledin the art will recognize, that the present disclosure embraces and isalso thereby described in terms of any individual member and anysubgroup of members of the Markush group, and Applicants reserve theright to revise the application or claims to refer specifically to anyindividual member or any subgroup of members of the Markush group.

It is to be understood that the terminology used herein is for thepurpose of describing specific embodiments only and is not intended tobe limiting. It is further to be understood that unless specificallydefined herein, the terminology used herein is to be given itstraditional meaning as known in the relevant art.

Reference throughout this specification to “one embodiment” or “anembodiment” and variations thereof means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment. Thus, the appearances of thephrases “in one embodiment” or “in an embodiment” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural referents, i.e., one or more,unless the content and context clearly dictates otherwise. For example,the term “a sensor” refers to one or more sensors, and the term “amedical device comprising a sensor” is a reference to a medical devicethat includes at least one sensor, where the medical device comprising asensor may have, for example, 1 sensor, 2 sensors, 3 sensors, 4 sensors,5 sensors, 6 sensors, 7 sensors, 8 sensors, 9 sensors, 10 sensors, ormore than 10 sensors. A plurality of sensors refers to more than onesensor. It should also be noted that the conjunctive terms, “and” and“or” are generally employed in the broadest sense to include “and/or”unless the content and context clearly dictates inclusivity orexclusivity as the case may be. Thus, the use of the alternative (e.g.,“or”) should be understood to mean either one, both, or any combinationthereof of the alternatives. In addition, the composition of “and” and“or” when recited herein as “and/or” is intended to encompass anembodiment that includes all of the associated items or ideas and one ormore other alternative embodiments that include fewer than all of theassociated items or ideas.

Unless the context requires otherwise, throughout the specification andclaims that follow, the word “comprise” and synonyms and variantsthereof such as “have” and “include”, as well as variations thereof suchas “comprises” and “comprising” are to be construed in an open,inclusive sense, e.g., “including, but not limited to.” The term“consisting essentially of” limits the scope of a claim to the specifiedmaterials or steps, or to those that do not materially affect the basicand novel characteristics of the claimed invention.

Any headings used within this document are only being utilized toexpedite its review by the reader, and should not be construed aslimiting the disclosure, invention or claims in any manner. Thus, theheadings and Abstract of the Disclosure provided herein are forconvenience only and do not interpret the scope or meaning of theembodiments.

Where a range of values is provided herein, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range is encompassed within the disclosure, invention or claims.The upper and lower limits of these smaller ranges may independently beincluded in the smaller ranges is also encompassed within thedisclosure, subject to any specifically excluded limit in the statedrange. Where the stated range includes one or both of the limits, rangesexcluding either or both of those included limits are also included inthe disclosure.

For example, any concentration range, percentage range, ratio range, orinteger range provided herein is to be understood to include the valueof any integer within the recited range and, when appropriate, fractionsthereof (such as one tenth and one hundredth of an integer), unlessotherwise indicated. Also, any number range recited herein relating toany physical feature, such as polymer subunits, size or thickness, areto be understood to include any integer within the recited range, unlessotherwise indicated. As used herein, the term “about” means ±20% of theindicated range, value, or structure, unless otherwise indicated.

All of the U.S. patents, U.S. patent application publications, U.S.patent applications, foreign patents, foreign patent applications andnon-patent publications referred to in this specification and/or listedin the Application Data Sheet, are incorporated herein by reference, intheir entirety. Such documents may be incorporated by reference for thepurpose of describing and disclosing, for example, materials andmethodologies described in the publications, which might be used inconnection with the present disclosure. The publications discussed aboveand throughout the text are provided solely for their disclosure priorto the filing date of the present application. Nothing herein is to beconstrued as an admission that the inventors are not entitled toantedate any referenced publication by virtue of prior invention.

All patents, publications, scientific articles, web sites, and otherdocuments and materials referenced or mentioned herein are indicative ofthe levels of skill of those skilled in the art to which the disclosurepertains, and each such referenced document and material is herebyincorporated by reference to the same extent as if it had beenincorporated by reference in its entirety individually or set forthherein in its entirety. Applicants reserve the right to physicallyincorporate into this specification any and all materials andinformation from any such patents, publications, scientific articles,web sites, electronically available information, and other referencedmaterials or documents.

In general, in the following claims, the terms used should not beconstrued to limit the claims to the specific embodiments disclosed inthe specification and the claims, but should be construed to include allpossible embodiments along with the full scope of equivalents to whichsuch claims are entitled. Accordingly, the claims are not limited by thedisclosure.

Furthermore, the written description portion of this patent includes allclaims. Furthermore, all claims, including all original claims as wellas all claims from any and all priority documents, are herebyincorporated by reference in their entirety into the written descriptionportion of the specification, and Applicants reserve the right tophysically incorporate into the written description or any other portionof the application, any and all such claims. Thus, for example, under nocircumstances may the patent be interpreted as allegedly not providing awritten description for a claim on the assertion that the precisewording of the claim is not set forth in haec verba in writtendescription portion of the patent.

The claims will be interpreted according to law. However, andnotwithstanding the alleged or perceived ease or difficulty ofinterpreting any claim or portion thereof, under no circumstances mayany adjustment or amendment of a claim or any portion thereof duringprosecution of the application or applications leading to this patent beinterpreted as having forfeited any right to any and all equivalentsthereof that do not form a part of the prior art.

Other nonlimiting embodiments are within the following claims. Thepatent may not be interpreted to be limited to the specific examples ornonlimiting embodiments or methods specifically and/or expresslydisclosed herein. Under no circumstances may the patent be interpretedto be limited by any statement made by any Examiner or any otherofficial or employee of the Patent and Trademark Office unless suchstatement is specifically and without qualification or reservationexpressly adopted in a responsive writing by Applicants.

As mentioned above, in the following claims, the terms used should notbe construed to limit the claims to the specific embodiments disclosedin the specification and the claims, but should be construed to includeall possible embodiments along with the full scope of equivalents towhich such claims are entitled. For example, described embodiments withone or more omitted components or steps can be additional embodimentscontemplated and covered by this application. Further in example, suchadditional embodiments can be the flow diagrams 1120 (FIG. 122), 1160(FIG. 123), and 1190 (FIG. 124) with one or more steps omitted.Similarly, described embodiments with one or more added components orsteps can be additional embodiments contemplated and covered by thisapplication. Further in example, such additional embodiments can be theflow diagrams 1120 (FIG. 122), 1160 (FIG. 123), and 1190 (FIG. 124) withone or more steps added. And described embodiments with one or moreomitted components or steps and one or more additional components orsteps can be additional embodiments contemplated and covered by thisapplication. Further in example, such additional embodiments can be theflow diagrams 1120 (FIG. 122), 1160 (FIG. 123), and 1190 (FIG. 124) withone or more steps omitted and one or more steps added. Accordingly, theclaims are not limited by the disclosure.

What is claimed is:
 1. A method for determining joint loosening in apatient having an implanted artificial joint, comprising a) analyzingmovement of an implanted artificial joint, and b) comparing saidmovement vs. previous/standardized norms.
 2. A method for determiningloosening of an implanted prosthesis in a patient having the implantedprosthesis, comprising: a) obtaining a standardized norm of movement byanalyzing movement of an implanted prosthesis during one or more firstmonitoring sessions; b) obtaining a current description of movement byanalyzing movement of an implanted prosthesis during one or more secondmonitoring sessions that occur subsequent to the one or more firstmonitoring sessions; and c) comparing said current description ofmovement to said standardized norm of movement, to thereby identifyloosening of an implanted prothesis in a patient having the implantedprosthesis.
 3. The method of claim 2 where the implanted prosthesiscomprises at least one sensor to measure the movement of the implantedartificial joint.
 4. The method of claim 3 wherein the at least onesensor is selected from an accelerometer and a gyroscope.
 5. The methodof claim 2 wherein the prosthesis is implanted in a knee of a subject.6. The method of claim 2 wherein the prosthesis is implanted in a hip ofa subject
 7. The method of claim 2 wherein the prosthesis is implantedin a shoulder of a subject.
 8. The method of claim 2 wherein theprosthesis comprises a sensor selected from an accelerometer and agyroscope.